High luminous efficiency and excellent thermal performance in rod-shaped YAG:Ce phosphor ceramics for laser lighting.

High power and high brightness laser lighting puts forward new requirements for phosphor converters such as high luminous efficiency, high thermal conductivity and high saturation threshold due to the severe thermal effect. The structure design of phosphor converters is proposed as what we believe to be a novel strategy for less heat production and more heat conduction. In this work, the rod-shaped YAG:Ce phosphor ceramics (PCs) and disc-shaped YAG:Ce PCs as control group were fabricated by the gel casting and vacuum sintering, to comparatively study the luminescence performance for LD lighting, on the premise that the total number of transverse Ce3+ ions and the volume of samples from two comparison groups were same. All rod YAG:Ce PCs with low Ce3+ concentration exhibited the high luminous efficiency and better thermal stability than YAG:Ce discs with high Ce3+ concentration. Under the laser power density of 47.8 W/mm2, the luminous saturation was never observed in all rod-shaped YAG:Ce PCs. The high luminous efficacy of 245∼274 lm/W, CRI of 56.3∼59.5 and CCT of 4509∼4478 K were achieved. More importantly, due to the extremely low Ce3+ doping concentration (0.01 at%), rod-shaped ceramics based LDs devices showed the excellent thermal performance and their surface temperatures were even below 30.5 °C surprisingly under the laser power density of 20.3 W·mm-2 (2 W). These results indicate that the rod shape of phosphor converter is a promising structure engineering for high power laser lighting.

Efficacy and safety of glucocorticoids use in patients with COVID-19: a systematic review and network meta­analysis.

BACKGROUND: Currently, some meta-analyses on COVID-19 have suggested that glucocorticoids use can reduce the mortality rate of COVID-19 patients, utilization rate of invasive ventilation, and improve the prognosis of patients. However, optimal regimen and dosages of glucocorticoid remain unclear. Therefore, the purpose of this network meta-analysis is to analyze the efficacy and safety of glucocorticoids in treating COVID-19 at regimens. METHODS: This meta-analysis retrieved randomized controlled trials from the earliest records to December 30, 2022, published in PubMed, Embase, Cochrane Library, CNKI Database and Wanfang Database, which compared glucocorticoids with placebos for their efficacy and safety in the treatment of COVID-19, Effects of different treatment regimens, types and dosages (high-dose methylprednisolone, very high-dose methylprednisolone, Pulse therapy methylprednisolone, medium-dose hydrocortisone, high-dose hydrocortisone, high-dose dexamethasone, very high-dose dexamethasone and placebo) on 28-day all-caused hospitalization mortality, hospitalization duration, mechanical ventilation requirement, ICU admission and safety outcome were compared. RESULTS: In this network meta-analysis, a total of 10,544 patients from 19 randomized controlled trials were finally included, involving a total of 9 glucocorticoid treatment regimens of different types and dosages. According to the analysis results, the 28-day all-cause mortality rate was the lowest in the treatment with pulse therapy methylprednisolone (OR 0.08, 95% CI 0.02, 0.42), but the use of high-dose methylprednisolone (OR 0.85, 95% CI 0.59, 1.22), very high-dose dexamethasone (OR 0.95, 95% CI 0.67, 1.35), high-dose hydrocortisone (OR 0.64, 95% CI 0.34, 1.22), medium-dose hydrocortisone (OR 0.80, 95% CI 0.49, 1.31) showed no benefit in prolonging the 28-day survival of patient. Compared with placebo, the treatment with very high-dose methylprednisolone (MD = -3.09;95%CI: -4.10, -2.08) had the shortest length of hospital stay, while high-dose dexamethasone (MD = -1.55;95%CI: -3.13,0.03) and very high-dose dexamethasone (MD = -1.06;95%CI: -2.78,0.67) did not benefit patients in terms of length of stay. CONCLUSIONS: Considering the available evidence, this network meta­analysis suggests that the prognostic impact of glucocorticoids in patients with COVID-19 may depend on the regimens of glucocorticoids. It is suggested that pulse therapy methylprednisolone is associated with lower 28-day all-cause mortality, very high-dose methylprednisolone had the shortest length of hospital stay in patients with COVID-19. TRIAL REGISTRATION: PROSPERO CRD42022350407 (22/08/2022).

A novel multi-frame wavelet generative adversarial network for scattering reconstruction of structured illumination microscopy.

Objective. Structured illumination microscopy (SIM) is widely used in various fields of life science research. In clinical practice, it has low phototoxicity, fast imaging speed and no special fluorescent markers. However, SIM is still affected by the scattering medium of biological tissues, resulting in insufficient resolution of the obtained images, which limits the development of life sciences. A novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of SIM.Approach. MWGAN is based on two components derived from the original image. A generative adversarial network constructed by wavelet transform is trained to reconstruct some complex details in the cell structure. Multi-frame adversarial network is used to obtain the inter-frame information of the image and use the complementary information of the before and after frames to improve the quality of the model reconstruction.Results. To demonstrate the robustness of MWGAN, multiple low-quality SIM image datasets are tested. Compared with the state-of-the-art methods, the proposed method achieves superior performance in both of the subjective and objective evaluation.Conclusion. MWGAN is effective for improving the clarity of SIM images. Meanwhile, the SIM images reconstructed by multiple frames improve the reconstruction quality of complex regions and allow clearer and dynamic observation of cellular functions.

SFWN: A Novel Semi-Supervised Feature Weighted Neural Network for Gene Data Feature Learning and Mining with Graph Modeling.

Gene expression data can serve for analyzing the genes with changed expressions, the correlation between genes and the influence of different circumstance on gene activities. However, labeling a large number of gene expression data is laborious and time-consuming. The insufficient labeled data pose a challenge to construct the deep learning model. Currently, some graph neural networks (GNN) based on semi-supervised learning mechanism only focus on the feature space and sample space of gene expression data, possibly affecting the accuracy. This paper puts forward a novel semi-supervised graph neural network model (SFWN). Firstly, we use the external knowledge of gene expression data for constructing a feature graph, a similarity kernel, and a sample graph for the first time. Later, a novel semi-supervised learning algorithm (SGA) is proposed to extract the data relationship and obtain the global sample structure better. A graph sparse module (SGCN) is also proposed to process sparse representation with gene expression data classification. To overcome the over smoothing problem, a new feature calculation method based on two spaces is proposed to feature representation analysis and calculation in this model. According to a lot of experiments and ablation studies conducted on several public datasets, SFWN exhibits a better effect and is superior to the state-of-the-art approaches (the accuracy and F1-Score are 0.9993 and 0.9899, respectively). Experimental results showed that the proposed SFWN model has strong gene expression feature learning and representation ability, and may provide a new insight and tool for relevant disease diagnosis and clinic practice.

Inhibition of sphingosine-1-phosphate receptor 3 suppresses ATP-induced NLRP3 inflammasome activation in macrophages via TWIK2-mediated potassium efflux.

Background: Inhibition of sphingosine kinase 1 (SphK1), which catalyzes bioactive lipid sphingosine-1-phosphate (S1P), attenuates NLRP3 inflammasome activation. S1P exerts most of its function by binding to S1P receptors (S1PR1-5). The roles of S1P receptors in NLRP3 inflammasome activation remain unclear. Materials and methods: The mRNA expressions of S1PRs in bone marrow-derived macrophages (BMDMs) were measured by real-time quantitative polymerase chain reaction (qPCR) assays. BMDMs were primed with LPS and stimulated with NLRP3 activators, including ATP, nigericin, and imiquimod. Interleukin-1ß (IL-1ß) in the cell culture supernatant was detected by enzyme-linked immunosorbent assay (ELISA). Intracellular potassium was labeled with a potassium indicator and was measured by confocal microscopy. Protein expression in whole-cell or plasma membrane fraction was measured by Western blot. Cecal ligation and puncture (CLP) was induced in C57BL/6J mice. Mortality, lung wet/dry ratio, NLRP3 activation, and bacterial loads were measured. Results: Macrophages expressed all five S1PRs in the resting state. The mRNA expression of S1PR3 was upregulated after lipopolysaccharide (LPS) stimulation. Inhibition of S1PR3 suppressed NLRP3 and pro-IL-1ß in macrophages primed with LPS. Inhibition of S1PR3 attenuated ATP-induced NLRP3 inflammasome activation, enhanced nigericin-induced NLRP3 activation, and did not affect imiquimod-induced NLRP3 inflammasome activation. In addition, inhibition of S1PR3 suppressed ATP-induced intracellular potassium efflux. Inhibition of S1PR3 did not affect the mRNA or protein expression of TWIK2 in LPS-primed BMDMs. ATP stimulation induced TWIK2 expression in the plasma membrane of LPS-primed BMDMs, and inhibition of S1PR3 impeded the membrane expression of TWIK2 induced by ATP. Compared with CLP mice treated with vehicle, CLP mice treated with the S1PR3 antagonist, TY52156, had aggravated pulmonary edema, increased bacterial loads in the lung, liver, spleen, and blood, and a higher seven-day mortality rate. Conclusions: Inhibition of S1PR3 suppresses the expression of NLRP3 and pro-IL-1ß during LPS priming, and attenuates ATP-induced NLRP3 inflammasome activation by impeding membrane trafficking of TWIK2 and potassium efflux. Although inhibition of S1PR3 decreases IL-1ß maturation in the lungs, it leads to higher bacterial loads and mortality in CLP mice.

Vision-based estimation of MDS-UPDRS scores for quantifying Parkinson's disease tremor severity.

Parkinson's disease (PD) is a common neurodegenerative movement disorder among older individuals. As one of the typical symptoms of PD, tremor is a critical reference in the PD assessment. A widely accepted clinical approach to assessing tremors in PD is based on part III of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, expert assessment of tremor is a time-consuming and laborious process that poses considerable challenges to the medical evaluation of PD. In this paper, we proposed a novel model, Global Temporal-difference Shift Network (GTSN), to estimate the MDS-UPDRS score of PD tremors based on video. The PD tremor videos were scored according to the majority vote of multiple raters. We used Eulerian Video Magnification (EVM) pre-processing to enhance the representations of subtle PD tremors in the videos. To make the model better focus on the tremors in the video, we proposed a special temporal difference module, which stacks the current optical flow to the result of inter-frame difference. The prediction scores were obtained from the Residual Networks (ResNet) embedded with a novel module, the Global Shift Module (GSM), which allowed the features of the current segment to include the global segment features. We carried out independent experiments using PD tremor videos of different body parts based on the scoring content of the MDS-UPDRS. On a fairly large dataset, our method achieved an accuracy of 90.6% for hands with rest tremors, 85.9% for tremors in the leg, and 89.0% for the jaw. An accuracy of 84.9% was obtained for postural tremors. Our study demonstrated the effectiveness of computer-assisted assessment for PD tremors based on video analysis. The latest version of the code is available at https://github.com/199507284711/PD-GTSN.

A Spatial-Temporal Graph Model for Pronunciation Feature Prediction of Chinese Poetry.

With the development of artificial intelligence, speech recognition and prediction have become one of the important research domains with wild applications, such as intelligent control, education, individual identification, and emotion analysis. Chinese poetry reading contains rich features of continuous pronunciations, such as mood, emotion, rhythm schemes, lyric reading, and artistic expression. Therefore, the prediction of the pronunciation characteristics of a Chinese poetry reading is the significance for the presentation of high-level machine intelligence and has the potential to create a high-level intelligent system for teaching children to read Tang poetry. Mel frequency cepstral coefficient (MFCC) is currently used to present important speech features. Due to the complexity and high degree of nonlinearity in poetry reading, however, there is a tough challenge facing accurate pronunciation feature prediction, that is, how to model complex spatial correlations and time dynamics, such as rhyme schemes. As for many current methods, they ignore the spatial and temporal characteristics in MFCC presentation. In addition, these methods are subjected to certain limitations on prediction for long-term performance. In order to solve these problems, we propose a novel spatial-temporal graph model (STGM-MHA) based on multihead attention for the purpose of pronunciation feature prediction of Chinese poetry. The STGM-MHA is designed using an encoder-decoder structure. The encoder compresses the data into a hidden space representation, while the decoder reconstructs the hidden space representation as output. In the model, a novel gated recurrent unit (GRU) module (AGRU) based on multihead attention is proposed to extract the spatial and temporal features of MFCC data effectively. The evaluation comparison of our proposed model versus state-of-the-art methods in six datasets reveals the clear advantage of the proposed model.

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications.

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

A Novel Elastomeric UNet for Medical Image Segmentation.

Medical image segmentation is of important support for clinical medical applications. As most of the current medical image segmentation models are limited in the U-shaped structure, to some extent the deep convolutional neural network (CNN) structure design is hard to be accomplished. The design in this study mimics the way the wave is elastomeric propagating, extending the structure from both the horizontal and spatial dimensions for realizing the Elastomeric UNet (EUNet) structure. The EUNet can be divided into two types: horizontal EUNet and spatial EUNet, based on the propagation direction. The advantages of this design are threefold. First, the training structure can be deepened effectively. Second, the independence brought by each branch (a U-shaped design) makes the flexible design redundancy available. Finally, a horizontal and vertical series-parallel structure helps on feature accumulation and recursion. Researchers can adjust the design according to the requirements to achieve better segmentation performance for the independent structural design. The proposed networks were evaluated on two datasets: a self-built dataset (multi-photon microscopy, MPM) and publicly benchmark retinal datasets (DRIVE). The results of experiments demonstrated that the performance of EUNet outperformed the UNet and its variants.

Rapid and Label-Free Prenatal Detection of Down's Syndrome Using Body Fluid Surface Enhanced Raman Spectroscopy.

Down's syndrome (DS) is the leading genetic cause of intellectual disability. In this work, the surface enhanced Raman spectroscopy (SERS) was used for the detection of amniotic fluid and plasma from pregnant women with DS fetus for the first time. High-quality and characteristic spectral features of amniotic fluid and plasma samples from DS groups can be obtained in comparison to normal group. Moreover, principal component analysis with linear discriminant analysis was applied to generate the efficient diagnostic model, achieving accuracies of 94.3% and 88.5% for the DS detection with amniotic fluid and plasma samples, respectively. This preliminary study would provide a novel, convenient and accurate prenatal test based on blood SERS technology for clinical DS screening.

Quantification of scar collagen texture and prediction of scar development via second harmonic generation images and a generative adversarial network.

Widely used for medical analysis, the texture of the human scar tissue is characterized by irregular and extensive types. The quantitative detection and analysis of the scar texture as enabled by image analysis technology is of great significance to clinical practice. However, the existing methods remain disadvantaged by various shortcomings, such as the inability to fully extract the features of texture. Hence, the integration of second harmonic generation (SHG) imaging and deep learning algorithm is proposed in this study. Through combination with Tamura texture features, a regression model of the scar texture can be constructed to develop a novel method of computer-aided diagnosis, which can assist clinical diagnosis. Based on wavelet packet transform (WPT) and generative adversarial network (GAN), the model is trained with scar texture images of different ages. Generalized Boosted Regression Trees (GBRT) is also adopted to perform regression analysis. Then, the extracted features are further used to predict the age of scar. The experimental results obtained by our proposed model are better compared to the previously published methods. It thus contributes to the better understanding of the mechanism behind scar development and possibly the further development of SHG for skin analysis and clinic practice.

Three-dimensional characterizations of two-photon excitation fluorescence images of elastic fibers affected by cutaneous scar duration.

BACKGROUND: The type or duration of a scar determines the choice of therapy available. Traditional detection methods can easily cause secondary trauma, so there is an urgent need for a non-invasive, rapid diagnostic approach. METHODS: A strategy for quantitative analysis of three-dimensional (3D) elastic fibers in human cutaneous scars was designed, which included 3D reconstruction, skeleton extraction, quantitative analysis, and random forest regression. RESULTS: Four reconstruction methods were used to reconstruct 3D two-photon excitation fluorescence images of elastic fibers for comparison. In the skeleton extraction stage, the 3D thinning algorithm was improved to prepare for accurate quantitative analysis, in which eight parameters comprising branches number (B-NUM), nodes number (N-NUM), averaged branch broken-line length (AB-BL), averaged linear branch length (AB-LL), averaged branch tortuosity (AB-T), branch direction consistency (B-DC), averaged branch volume (AB-V), and averaged branch sectional area (AB-SA) were presented. Six of them, except averaged branch tortuosity (AB-T) and branch direction consistency (B-DC), showed an explicit tendency to change with scar duration. In the random forests regression analysis, the six extracted parameters could be used to predict scar duration with R2=0.981 and RMSE=0.513. CONCLUSIONS: The parameters we extracted had a distinct relationship with scar duration, and random forests regression showed better performance in forecasting scar duration than unitary models.

Global Trends in Research of Macrophages Associated With Acute Lung Injury Over Past 10 Years: A Bibliometric Analysis.

Acute lung injury (ALI) is an intractable disorder associated with macrophages. This bibliometric analysis was applied to identify the characteristics of global scientific output, the hotspots, and frontiers about macrophages in ALI over the past 10 years. We retrieved publications published from 2011 to 2020 and their recorded information from Science Citation Index Expanded (SCI-expanded) of Web of Science Core Collection (WoSCC). Bibliometrix package was used to analyze bibliometric indicators, and the VOSviewer was used to visualize the trend and hotspots of researches on macrophages in ALI. Altogether, 2,632 original articles were reviewed, and the results showed that the annual number of publications (Np) concerning the role of macrophages in ALI kept increasing over the past 10 years. China produced the most papers, the number of citations (Nc) and H-index of the USA ranked first. Shanghai Jiaotong University and INT IMMUNOPHARMACOL were the most prolific affiliation and journal, respectively. Papers published by Matute-Bello G in 2011 had the highest local citation score (LCS). Recently, the keywords "NLRP3" and "extracellular vesicles" appeared most frequently. Besides, researches on COVID-19-induced ALI related to macrophages seemed to be the hotspot recently. This bibliometric study revealed that publications related to macrophages in ALI tend to increase continuously. China was a big producer and the USA was an influential country in this field. Most studies were mainly centered on basic researches in the past decade, and pathways associated with the regulatory role of macrophages in inhibiting and attenuating ALI have become the focus of attention in more recent studies. What is more, our bibliometric analysis showed that macrophages play an important role in COVID-19-induced ALI and may be a target for the treatment of COVID-19.

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program.

INTRODUCTION: Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes. RESEARCH DESIGN AND METHODS: Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility. RESULTS: Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study. CONCLUSIONS: NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study. TRIAL REGISTRATION NUMBER: Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.

Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy.

Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.

A target-triggered and self-calibration aptasensor based on SERS for precise detection of a prostate cancer biomarker in human blood.

Sensitive and precise detection of prostate-specific antigen (PSA) is critical for prostate cancer screening and monitoring. Herein, a target-triggered and self-calibration aptasensor based on a core-satellite nanostructure using surface-enhanced Raman spectroscopy (SERS) technology was developed for the sensitive and reliable determination of PSA protein, with a limit of detection of 0.38 ag mL-1 and a dynamic detection range of 10-2 to 10-15 mg mL-1. Furthermore, the proposed approach for the detection of PSA in patient blood samples was performed, and results showed that it is capable of providing comparable detection accuracy associated with a larger dynamic detection range and a lower detection limit as well as less sample requirement (only 5 µL) in comparison with the clinical commonly used method. Therefore, this SERS-based aptasensor for the detection of PSA in human blood samples has promising potential to be an alternative tool for clinical application in the accurate screening of prostate cancer.

Purification and characterization of bromelain from pineapple (Ananas comosus L.) peel waste.

Bromelain is widely used in food industry and pharmaceutical products due to its strong antioxidant properties. Therefore, the extraction of bromelain from pineapple peel may improve the profitability and sustainability of pineapple industry. The aim of this work is to show the purification, stability, and kinetics of bromelain from pineapple peel. By studying the stability of purified bromelain (PB), we found that the activity of PB was inhibited by Fe3+ , Al3+ , methanol, ethanol, and n-butyl alcohol, while it was increased in the presence of Ca2+ , ethylenediamine tetra acetic acid, glucose, D-xylose, maltose, potassium sodium tartrate, sodium citrate, citric acid, and sodium nitrite. These stability tests will expand the application and space acquisition of bromelain. The kinetics study indicated that the thermal inactivation of PB was conforming to the first-order reaction and the half-life (t1/2 ) of PB under different temperature conditions (45, 55, 65, and 75 °C) was 81.54, 31.12, 10.28, and 5.23 min, respectively. Therefore, the inactivation time of PB can be predicted at different temperatures for food heating processing. PRACTICAL APPLICATION: The potential of utilizing pineapple peel for bromelain extraction might improve the profitability and sustainability of the pineapple industry.

Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network.

BACKGROUND: Multiphoton microscopy (MPM) offers a feasible approach for the biopsy in clinical medicine, but it has not been used in clinical applications due to the lack of efficient image processing methods, especially the automatic segmentation technology. Segmentation technology is still one of the most challenging assignments of the MPM imaging technique. METHODS: The MPM imaging segmentation model based on deep learning is one of the most effective methods to address this problem. In this paper, the practicability of using a convolutional neural network (CNN) model to segment the MPM image of skin cells in vivo was explored. A set of MPM in vivo skin cells images with a resolution of 128×128 was successfully segmented under the Python environment with TensorFlow. A novel deep-learning segmentation model named Dense-UNet was proposed. The Dense-UNet, which is based on U-net structure, employed the dense concatenation to deepen the depth of the network architecture and achieve feature reuse. This model included four expansion modules (each module consisted of four down-sampling layers) to extract features. RESULTS: Sixty training images were taken from the dorsal forearm using a femtosecond Ti:Sa laser running at 735 nm. The resolution of the images is 128×128 pixels. Experimental results confirmed that the accuracy of Dense-UNet (92.54%) was higher than that of U-Net (88.59%), with a significantly lower loss value of 0.1681. The 90.60% Dice coefficient value of Dense-UNet outperformed U-Net by 11.07%. The F1-Score of Dense-UNet, U-Net, and Seg-Net was 93.35%, 90.02%, and 85.04%, respectively. CONCLUSIONS: The deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM in vivo images had been adopted by introducing a deep CNN to bridge this gap in Dense-UNet technology. Dense-UNet has reached ultramodern performance for MPM images, especially for in vivo images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images in vivo.

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction.

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

A lanthanide ion-doped Ba3Sc2F12 phosphor: hydrothermal synthesis, morphological control, energy transfer, and temperature-sensing performance.

Ba3Sc2F12 crystals were synthesized by a facile one-step hydrothermal method with Ba/Sc raw material in a ratio of 3 : 2. With the F/Sc ratio increasing from 4 to 8, the obtained crystal's morphology evolved gradually from a strip to a chocolate shape; further, the use of the additives, such as CTAB and EDTA, the obtained crystal's morphology changed from strip to cubic. The energy transfer of Ce3+→ Tb3+ in the Ba3Sc2F12 host was also explored, and it was found to belong to a dipole-dipole interaction mechanism; the color of the light could be adjusted from blue-violet to green due to the different energy transfer efficiencies at different Ce3+ and Tb3+ ion-doping concentrations. Because of the thermal coupling level of Er3+ (2H11/2→4I15/2 and 4S3/2→4I15/2), Ba3Sc2F12:14%Yb3+,2%Er3+ phosphors showed an excellent temperature-sensing ability with SA(max) = 0.0043 K-1 and Tmax = 523 K, which are much better than the previously reported values for Yb3+/Er3+ co-doped systems. The as-prepared lanthanide ion-doped phosphors might have potential to serve as color light/displays and temperature control/sensors.