Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy.

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

Microplastics footprint in nature reserves-a case study on the microplastics in the guano from Yancheng Wetland Rare Birds National Nature Reserve, China.

Bio-ingestion of microplastics poses a global threat to ecosystems, yet studies within nature reserves, crucial habitats for birds, remain scarce despite the well-documented ingestion of microplastics by avian species. Located in Jiangsu Province, China, the Yancheng Wetland Rare Birds Nature Reserve is home to diverse bird species, including many rare ones. This study aimed to assess the abundance and characteristics of microplastics in common bird species within the reserve, investigate microplastic enrichment across different species, and establish links between birds' habitat types and microplastic ingestion. Microplastics were extracted from the feces of 110 birds, with 84 particles identified from 37.27% of samples. Among 8 species studied, the average microplastic abundance ranged from 0.97 ± 0.47 to 43.43 ± 61.98 items per gram of feces, or 1.5 ± 0.87 to 3.4 ± 1.50 items per individual. The Swan goose (Anser cygnoides) exhibited the highest microplastic abundance per gram of feces, while the black-billed gull (Larus saundersi) had the highest abundance per individual. The predominant form of ingested microplastics among birds in the reserve was fibers, with polyethylene being the most common polymer type. Significant variations in plastic exposure were observed among species and between aquatic and terrestrial birds. This study represents the first quantitative assessment of microplastic concentrations in birds within the reserve, filling a crucial gap in research and providing insights for assessing microplastic pollution and guiding bird conservation efforts in aquatic and terrestrial environments.

Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images.

Purpose: To develop and validate a deep learning system (DLS) for estimation of vertical cup-to-disc ratio (vCDR) in ultra-widefield (UWF) and smartphone-based fundus images. Methods: A DLS consisting of two sequential convolutional neural networks (CNNs) to delineate optic disc (OD) and optic cup (OC) boundaries was developed using 800 standard fundus images from the public REFUGE data set. The CNNs were tested on 400 test images from the REFUGE data set and 296 UWF and 300 smartphone-based images from a teleophthalmology clinic. vCDRs derived from the delineated OD/OC boundaries were compared with optometrists' annotations using mean absolute error (MAE). Subgroup analysis was conducted to study the impact of peripapillary atrophy (PPA), and correlation study was performed to investigate potential correlations between sectoral CDR (sCDR) and retinal nerve fiber layer (RNFL) thickness. Results: The system achieved MAEs of 0.040 (95% CI, 0.037-0.043) in the REFUGE test images, 0.068 (95% CI, 0.061-0.075) in the UWF images, and 0.084 (95% CI, 0.075-0.092) in the smartphone-based images. There was no statistical significance in differences between PPA and non-PPA images. Weak correlation (r = -0.4046, P < 0.05) between sCDR and RNFL thickness was found only in the superior sector. Conclusions: We developed a deep learning system that estimates vCDR from standard, UWF, and smartphone-based images. We also described anatomic peripapillary adversarial lesion and its potential impact on OD/OC delineation. Translational Relevance: Artificial intelligence can estimate vCDR from different types of fundus images and may be used as a general and interpretable screening tool to improve community reach for diagnosis and management of glaucoma.

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.

Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction. This study proposes an unsupervised and subject-specific DL method for QSM reconstruction based on implicit neural representation (INR), referred to as INR-QSM. INR has emerged as a powerful framework for learning a high-quality continuous representation of the signal (image) by exploiting its internal information without training labels. In INR-QSM, the desired susceptibility map is represented as a continuous function of the spatial coordinates, parameterized by a fully-connected neural network. The weights are learned by minimizing a loss function that includes a data fidelity term incorporated by the physical model and regularization terms. Additionally, a novel phase compensation strategy is proposed for the first time to account for the non-local effect of tissue phase in data consistency calculation to make the physical model more accurate. Our experiments show that INR-QSM outperforms traditional established QSM reconstruction methods and the compared unsupervised DL method both qualitatively and quantitatively, and is competitive against supervised DL methods under data perturbations.

An Adaptive Multi-Population Approach for Sphericity Error Evaluation in the Manufacture of Hemispherical Shell Resonators.

The performance of a hemispherical resonant gyroscope (HRG) is directly affected by the sphericity error of the thin-walled spherical shell of the hemispherical shell resonator (HSR). In the production process of the HSRs, high-speed, high-accuracy, and high-robustness requirements are necessary for evaluating sphericity errors. We designed a sphericity error evaluation method based on the minimum zone criterion with an adaptive number of subpopulations. The method utilizes the global optimal solution and the subpopulations' optimal solution to guide the search, initializes the subpopulations through clustering, and dynamically eliminates inferior subpopulations. Simulation experiments demonstrate that the algorithm exhibits excellent evaluation accuracy when processing simulation datasets with different sphericity errors, radii, and numbers of sampling points. The uncertainty of the results reached the order of 10-9 mm. When processing up to 6000 simulation datasets, the algorithm's solution deviation from the ideal sphericity error remained around -3 × 10-9 mm. And the sphericity error evaluation was completed within 1 s on average. Additionally, comparison experiments further confirmed the evaluation accuracy of the algorithm. In the HSR sample measurement experiments, our algorithm improved the sphericity error assessment accuracy of the HSR's inner and outer contour sampling datasets by 17% and 4%, compared with the results given by the coordinate measuring machine. The experiment results demonstrated that the algorithm meets the requirements of sphericity error assessment in the manufacturing process of the HSRs and has the potential to be widely used in the future.

Satellite observations of suspended particulate matter concentration in Lake Gaoyou in the past four decades.

Suspended Particulate Matter (SPM) concentration stands as a pivotal determinant of water quality within lake ecosystems. However, comprehension of the enduring dynamics of SPM within lakes is severely hindered due to a shortage of long-term records. Our research has developed a robust remote sensing algorithm to retrieve the SPM concentration in Lake Gaoyou, situated in the lower reaches of the Huai River basin in China. The algorithm demonstrates commendable performance, with an uncertainty of 28.68 %. Leveraging Landsat series sensors imagery, our investigation yields high spatial resolution SPM concentration maps, which first provide a four-decades record of the SPM distribution within Lake Gaoyou. Our findings unveil a significant annual reduction of 1.35 mg L-1 in SPM concentration over the past four decades. This notable decline is probably attributable to a series of ecological initiatives to enhancing the management of the eco-friendly within the basin. Furthermore, our research delineated the influence of environmental factors on the intra-annual SPM dynamics across distinct spatial domains, encompassing the natural inlet region, semi-obstructed inlet region and outlet areas within the lake The SPM concentration in the natural inlet region exhibits a conspicuous correlation with precipitation. Increased precipitation induces runoff within the basin, facilitating the transport of suspended solids and sediment into the lake, consequently augmenting SPM levels. Conversely, the semi-obstructed inlet and outlet areas are predominantly influenced by the wind field, with variations in SPM attributed to sediment resuspension caused by water mixing driven by wind forcing. Our research can be considered an important reference to the evaluation of the management of the lake over long periods.

Assessing the Feasibility and Safety of the Toumai® Robotic System in Urologic Surgery: Initial Experience.

Purpose Robotic surgery using da Vinci surgical system has gained prominence in urology, emerging robotic platforms are expanding its applications and increasing affordability. We assess the feasibility and safety of a novel system, the Toumai® robotic system in various urological surgeries. Methods This prospective study was conducted at the first affiliated hospital of Zhengzhou university. Twenty consecutive patients underwent renal and prostatic surgery with the Toumai®. The study assessed technical feasibility (conversion rate) and safety (perioperative complications) of the procedures as primary outcomes. Secondary endpoints included key surgical perioperative outcomes; functional and oncologic results. The Endoscopic Surgical System operates within a master-slave protocol, comprising a Surgeon Console, Patient Platform, and Vision Platform Results Seventeen patients underwent various nephrectomy procedures and three underwent radical prostatectomy. There was no conversion to alternative surgical approach, a single (Clavien-Dindo grade ≥3b) complication occurred and no readmission was recorded within 30 days. The median operative time was 120, 140, and 210 minutes for partial nephrectomy, radical nephrectomy and radical prostatectomy respectively. Off clamp partial nephrectomy was performed in one case and the warm ischemia time in the remaining two case was eighteen minutes. The median docking time was 22 minutes for nephrectomy and 20 minutes for radical prostatectomy, no major robotic malfunction was encountered. At three months follow-up, no tumor recurrence was recorded, renal function was well preserved and the continence status was satisfactory. Conclusions We present the initial clinical utilization of an innovative robotic platform. Complex urological interventions were successfully completed without conversions and with minimal complications. Further investigations are warranted to confirm these initial findings.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application.

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

Gamma-ray irradiation as an effective method for mitigating antibiotic resistant bacteria and antibiotic resistance genes in aquatic environments.

The prevalence of antibiotic resistance genes (ARGs) in municipal wastewater treatment plants (MWTPs) has emerged as a significant environmental concern. Despite advanced treatment processes, high levels of ARGs persist in the secondary effluent from MWTPs, posing ongoing environmental risks. This study explores the potential of gamma-ray irradiation as a novel approach for sterilizing antibiotic-resistant bacteria (ARB) and reducing ARGs in MWTP secondary effluent. Our findings reveal that gamma-ray irradiation at an absorbed dose of 1.6 kGy effectively deactivates all culturable bacteria, with no subsequent revival observed after exposure to 6.4 kGy and a 96-h incubation in darkness at room temperature. The removal efficiencies for a range of ARGs, including tetO, tetA, blaTEM-1, sulI, sulII, and tetW, were up to 90.5% with a 25.6 kGy absorbed dose. No resurgence of ARGs was detected after irradiation. Additionally, this study demonstrates a considerable reduction in the abundances of extracellular ARGs, with the transformation efficiencies of extracellular tetracycline and sulfadiazine resistance genes decreasing by 56.3-81.8% after 25.6 kGy irradiation. These results highlight the effectiveness of gamma-ray irradiation as an advanced and promising method for ARB sterilization and ARG reduction in the secondary effluent of MWTPs, offering a potential pathway to mitigate environmental risks associated with antibiotic resistance.

GSTP1 is a negative regulator of MAVS in the antiviral signaling against SVCV infection.

Glutathione S-transferase P1 (GSTP1), the most ubiquitous member of the GST superfamily, plays vital roles in the detoxification, antioxidant defense, and modulation of inflammatory responses. However, limited studies have been conducted on the function of GSTP1 in antiviral innate immunity. In this study, we have cloned the homolog of GSTP1 in triploid hybrid crucian carp (3nGSTP1) and investigated its regulatory role in the interferon signaling pathway. The open reading frame of 3nGSTP1 is composed of 627 nucleotides, encoding 209 amino acids. In response to spring viremia of carp virus (SVCV) infection, the mRNA level of 3nGSTP1 was up-regulated in the liver, kidney, and caudal fin cell lines (3 nF C) of triploid fish. The knockdown of 3nGSTP1 in 3 nF C improved host cell's antiviral capacity and attenuated SVCV replication. Additionally, overexpression of 3nGSTP1 inhibited the activation of IFN promoters induced by SVCV infection, poly (I:C) stimulation, or the RLR signaling factors. The co-immunoprecipitation assays further revealed that 3nGSTP1 interacts with 3nMAVS. In addition, 3nGSTP1 dose-dependently inhibited 3nMAVS-mediated antiviral activity and reduced 3nMAVS protein level. Mechanistically, 3nGSTP1 promoted ubiquitin-proteasome degradation of MAVS by promoting its K48-linked polyubiquitination. To conclude, our results indicate that GSTP1 acts as a novel inhibitor of MAVS, which negatively regulates the IFN signaling.

Effect of various oligosaccharides on casein solubility and other functional properties: Via Maillard reaction.

This study explored the improvement of casein (CN)'s properties by conjugating it with oligosaccharides, namely, fructooligosaccharide (FOS), galactooligosaccharide (GOS), isomaltooligosaccharide (IMO), and xylo-oligosaccharide (XOS) via Maillard reaction to identify the most optimal oligosaccharides and modification conditions. The degree of grafting was 30.5 ± 0.41 % for CN-FOS, 33.7 ± 0.62 % for CN-GOS, 38.9 ± 0.51 % for CN-IMO, and 43.7 ± 0.54 % for CN-XOS. With the degree of grafting rising, more oligosaccharides were conjugated, causing greater changes in CN properties. The CN-XOS underwent significant alterations, as the introduction of oligosaccharides led to a decrease in particle size by around 51 nm. Furthermore, the hydroxyl groups caused a reduction in surface hydrophobicity, which in turn decreased the proportion of hydrophobic groups. The solubility of CN-XOS increased significantly at pH 3, by approximately 30.99 %. Additionally, the conjugation of oligosaccharides substantially boosted the rates of DPPH, ABTS, and -OH radical scavenging by 4.61 times, 2.20 times, and 2.58 times, respectively, and also improved the thermal stability of the modified CN. Moreover, the process lowered the protein digestibility, possibly enhancing its applicability as an active substance transporter. This research offers additional theoretical backing for altering CN with oligosaccharides and implementing it in the food and pharmaceutical sectors.

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.

RATIONALE AND OBJECTIVES: To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. MATERIALS AND METHODS: We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. RESULTS: The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machine achieved the best performance with an 0.99 area under the curve (AUC) of (95% confidence interval (CI), 0.98-1.00) and 0.83 (95% CI, 0.73-0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62-0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99-1.00). CONCLUSION: In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC.

Facet-Specific Photocatalytic Degradation of Extracellular Antibiotic Resistance Genes by Hematite Nanoparticles in Aquatic Environments.

The persistence of extracellular antibiotic resistance genes (ARGs) in aquatic environments has attracted increasing attention due to their potential threat to public health and the environment. However, the fate of extracellular ARGs in receiving water remains largely unknown. This study investigated the influence of hematite nanoparticles, a widespread natural mineral, on the photodegradation of extracellular ARGs in river water. Results showed that under exposure to visible light, hematite nanoparticles, at environmental concentrations, resulted in a 3-5 orders of magnitude reduction in extracellular ARGs. This photodegradation of extracellular ARGs is shown to be facet-dependent; the (001) facet of hematite demonstrates a higher removal rate than that of the (100) facet, which is ascribed to its enhanced adsorption capability and higher hydroxyl radical (•OH) production. Density functional theory (DFT) calculations corroborate this finding, indicating elevated iron density, larger adsorption energy, and lower energy barrier of •OH formation on the (001) facet, providing more active sites and •OH generation for extracellular ARG interaction. Gel electrophoresis and atomic force microscopy analyses further confirm that the (001) facet causes more substantial damage to extracellular ARGs than the (100) facet. These findings pave the way for predicting the photodegradation efficiency of hematite nanoparticles with varied facets, thereby shedding light on the inherent self-purification capacity for extracellular ARGs in both natural and engineered aquatic environments.

Research Progress on Ferroptosis in Organic Erectile Dysfunction

Erectile dysfunction (ED) is a common sexual dysfunction in men that can occur with the onset of sexual activity or even earlier,and the development of ED involves a variety of pathophysiologic mechanisms. Organic erectile dysfunction refers to a typeof erectile dysfunction that is primarily caused by physical or organic factors rather than psychological or emotional factors.Worldwide, the incidence and prevalence of ED are high. Currently, the mainstay of ED treatment is the use of medications suchas phosphodiesterase type 5 inhibitors (PDE5Is). However, these medications cause adverse effects such as flushing, indigestionand headaches and are not effective for some ED patients. Therefore, there is an urgent need to explore new targets of actionfor the treatment of ED. Ferroptosis is a type of iron-dependent regulated cell death initiated by lipid peroxidation and is a novelform of programmed cell death associated with the pathogenesis of various diseases. Prior research has provided evidence thatthe ferroptosis pathway plays a pivotal role in the modulation of ED, establishing this pathway as a significant foundation for thedevelopment of potential therapeutic interventions for ED. Experiments have shown that the inhibition of ferroptosis can improveED. This article systematically introduces the role and influence of ferroptosis in various types of organic erectile dysfunctionand describes the molecular mechanism, related pathways, and potential targets, providing a theoretical basis for the clinical diagnosis and treatment of ED. (AU)

Frequency suppression of director oscillations in AC-driven liquid-crystal-based terahertz phase shifters.

Frequency-induced instability is widely present in nematic liquid crystals (LCs), which poses a problem in improving liquid-crystal-based phase-shift devices driven by alternating currents. Herein, the Fréedericksz transition of thick nematic LCs was investigated under alternating electric fields to reveal the suppression of frequency-induced instability in the low frequency range. By extending the Frank-Leslie equation to the AC-driven case, the response of the LC was numerically calculated, and the frequency threshold for suppressing the driven instability was estimated in conjunction with the perturbation method. Experimentally, the frequency suppression of LC fluctuations was verified by using applied electric fields. In addition, the root-mean-square-error of the refractive index was measured to be less than 2 × 10-5, which excludes the convective instability-generating domains in devices. It was revealed that the fabricated thick LC phase shifters provided a phase shift of more than 360° at 2 THz and can be used in the terahertz band. It was observed that the electrically driven phase-shift characteristics were in accordance with the theoretical results as the threshold frequency condition was satisfied. This work provides an experimental and theoretical reference for improving modulation performance and enhancing the characterization of AC-driven LC-based phase-shift devices.

Effect of in situ ultrasonic wave and influent ammonia nitrogen fluctuation on stability of aerobic granular sludge.

This study elucidates the impact of fluctuating influent conditions and in situ ultrasonic wave exposure on the stability of aerobic granular sludge (AGS) in the treatment of simulated wastewater emanating from rare earth mining operations. During a stable influent period spanning from Day 1 to Day 95, the seed granules underwent an initial disintegration followed by a re-granulation phase. The secondary granulation was achieved on Day 80 and Day 40 for the ultrasonic reactor (R1) and the control reactor (R2), respectively. Notably, granules formed in R1 exhibited a more porous structure compared to those generated in R2. Subsequently, when the ammonia nitrogen in the influent oscillated between 100 and 500 mg/L during Days 96-140, both reactors yielded compact and densely structured granules. Nitrogen removal profiles were comparable between the two reactors: the removal efficiencies for ammonia nitrogen and total inorganic nitrogen escalated from 95% and 80%, respectively, during Days 1-95, to 95% and 90%, respectively, post-Day 140. A suite of performance metrics indicated that steady-state granules from R1 outperformed those from R2 across several parameters. Specifically, the nitrification/denitrification rates, and relative abundance of denitrifying bacteria were all higher in granules from R1. Conversely, the relative abundance of nitrifying bacteria was comparable between granules from both reactors. However, R1 granules demonstrated lower sludge concentration and smaller average particle size than their R2 counterparts. In conclusion, the AGS system demonstrated robust resilience to fluctuating ammonia nitrogen, and the application of ultrasonic waves significantly enhanced granular activity while achieving in situ sludge reduction.

Impact of Community Eye Clinics (CEC) on Specialist Eye Clinic Referrals.

PURPOSE: Community Eye Clinics (CEC) increase accessibility of specialist ophthalmic services in the community, reducing demand for tertiary eye services. This paper aims to evaluate the impact of CEC on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC). METHODS: A retrospective analysis was performed on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC) over a similar 3-months period before and after the introduction of CEC in August 2018 (1 January to 31 March in 2018 and 2019, respectively). Data pertaining to patients' presenting complaints, referral reasons, final diagnoses, follow-up plans, and need for ophthalmic intervention were obtained. RESULTS: We included 978 patients in our study. There was a 27.5% reduction in the number of first-visit referrals seen at SOC after the establishment of CEC. Patients were more likely to be referred on to sub-specialty eye clinics (10.8% vs. 12.9%, p= p = .304) and receive more ophthalmic interventions (15% vs. 16.3%, p = .066) than prior to CEC. CONCLUSION: The CEC provides greater accessibility to eye care within the community. Optometrists are upskilled to manage patients with stable eye conditions, whilst eye specialists can provide timely care to the SOC for patients with more severe eye conditions.

Real-Time Detection Algorithm for Kiwifruit Canker Based on a Lightweight and Efficient Generative Adversarial Network.

Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.

Sub-voxel quantitative susceptibility mapping for assessing whole-brain magnetic susceptibility from ages 4 to 80.

The evolution of magnetic susceptibility of the brain is mainly determined by myelin in white matter (WM) and iron deposition in deep gray matter (DGM). However, existing imaging techniques have limited abilities to simultaneously quantify the myelination and iron deposition within a voxel throughout brain development and aging. For instance, the temporal trajectories of iron in the brain WM and myelination in DGM have not been investigated during the aging process. This study aimed to map the age-related iron and myelin changes in the whole brain, encompassing myelin in DGM and iron deposition in WM, using a novel sub-voxel quantitative susceptibility mapping (QSM) method. To achieve this, a cohort of 494 healthy adults (18-80 years old) was studied. The sub-voxel QSM method was employed to obtain the paramagnetic and diamagnetic susceptibility based on the approximated R 2 ' map from acquired R 2 * map. The linear relationship between R 2 * and R 2 ' maps was established from the regression coefficients on a small cohort data acquired with both 3D gradient recalled echo data and R 2 mapping. Large cohort sub-voxel susceptibility maps were used to create longitudinal and age-specific atlases via group-wise registration. To explore the differential developmental trajectories in the DGM and WM, we employed nonlinear models including exponential and Poisson functions, along with generalized additive models. The constructed atlases reveal the iron accumulation in the posterior part of the putamen and the gradual myelination process in the globus pallidus with aging. Interestingly, the developmental trajectories show that the rate of myelination differs among various DGM regions. Furthermore, the process of myelin synthesis is paralleled by an associated pattern of iron accumulation in the primary WM fiber bundles. In summary, our study offers significant insights into the distinctive developmental trajectories of iron in the brain's WM and myelination/demyelination in the DGM in vivo. These findings highlight the potential of using sub-voxel QSM to uncover new perspectives in neuroscience and improve our understanding of whole-brain myelination and iron deposit processes across the lifespan.

hnRNPM suppressed IRF7-mediated IFN signaling in the antiviral innate immunity in triploid hybrid fish.

Mammalian heterogeneous nuclear ribonucleoproteins M (hnRNPM) is a critical splicing regulatory protein that has been reported to negatively regulate the RLR signaling pathway by impairing the binding of RIG-I and MDA5 to viral RNA. To explore the role of hnRNPM in the antiviral innate immune response in teleost fish, the hnRNPM homologue of triploid fish (3nhnRNPM) has been cloned and identified in this paper. The CDS of 3nhnRNPM gene is composed of 2016 nucleotides and encodes 671 amino acids. 3nhnRNPM migrated around 71 kDa in immunoblotting assay and was mainly detected in the nucleus in nucleo-cytoplasmic separation assay and immunofluorescent staining test. When 3nhnRNPM and 3nIRF7 were co-expressed in EPC cells, 3nhnRNPM significantly reduced the 3nIRF7-induced interferon (IFN) promoter transcription. Correspondingly, the mRNA levels of the SVCV-M, -N, -P, and -G genes were noteworthily enhanced, but the transcription levels of epcIFNφ1, epcMx1, epcPKR, and epcISG15 were dramatically decreased. Additionally, the knockdown of 3nhnRNPM resulted in restricted SVCV replication and enhanced host cell antiviral activity. Furthermore, the association between 3nhnRNPM and 3nIRF7 has been identified by the co-immunoprecipitation assay. In addition, we found that 3nIRF7 was detained in the nucleus when co-expressed with 3nhnRNPM. To sum up, our data supported the conclusion that 3nhnRNPM suppressed 3nIRF7-mediated IFN signaling in the antiviral innate immunity.