Insight into N2O emission and denitrifier communities under different aeration intensities in composting of cattle manure from perspective of multi-factor interaction analysis.

Nitrous oxide (N2O) emission from composting is a significant contributor to greenhouse effect and ozone depletion, which poses a threat to environment. To address the challenge of mitigating N2O emission during composting, this study investigated the response of N2O emission and denitrifier communities (detected by metagenome sequencing) to aeration intensities of 6 L/min (C6), 12 L/min (C12), and 18 L/min (C18) in cattle manure composting using multi-factor interaction analysis. Results showed that N2O emission occurred mainly at mesophilic phase. Cumulative N2O emission (QN2O, 9.79 mg·kg-1 DW) and total nitrogen loss (TN loss, 16.40 %) in C12 composting treatment were significantly lower than those in the other two treatments. The lower activity of denitrifying enzymes and the more complex and balanced network of denitrifiers and environmental factors might be responsible for the lower N2O emission. Denitrification was confirmed to be the major pathway for N2O production. Moisture content (MC) and Luteimonas were the key factors affecting N2O emission, and nosZ-carrying denitrifier played a significant role in reducing N2O emission. Although relative abundance of nirS was lower than that of nirK significantly (P < 0.05), nirS was the key gene influencing N2O emission. Community composition of denitrifier varied significantly with different aeration treatments (R2 = 0.931, P = 0.001), and Achromobacter was unique to C12 at mesophilic phase. Physicochemical factors had higher effect on QN2O, whereas denitrifying genes, enzymes and NOX- had lower effect on QN2O in C12. The complex relationship between N2O emission and the related factors could be explained by multi-factor interaction analysis more comprehensively. This study provided a novel understanding of mechanism of N2O emission regulated by aeration intensity in composting.

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph.

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

Improving prediction of N2O emissions during composting using model-agnostic meta-learning.

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Comparison of the efficacy for early warning systems in predicting obstetric critical illness.

OBJECTIVE: To validate the accuracy of four early warning scores for early identification of women at risk. METHODS: This was a retrospective study of pregnant women admitted in obstetrics Critical Care Unit (ICU). Capacity of the Modified Obstetric Early Warning Score (MOEWS), ICNARC Obstetric Early Warning Score (OEWS), Maternal Early Obstetric Warning System (MEOWS chart), and Maternal Early Warning Trigger (MEWT) were compared in predicting severe maternal morbidity. Area under receiver operator characteristic (AUROC) curve was used to evaluate the predictive performance of scoring system. RESULTS: A total of 352 pregnant women were enrolled and 290 were identified with severe maternal morbidity. MOEWS was more sensitive than MEOWS chart, ICNARC OEWS and MEWT (96.9 % vs. 83.4 %, 66.6 % and 44.8 %). MEWT had the highest specificity (98.4 %), followed by MOEWS (83.9 %), ICNARC OEWS (75.8 %) and MEOWS chart (48.4 %). AUROC of MOEWS, ICNARC OEWS, MEOWS chart, and MEWT for prediction of maternal mortality were 0.91 (95 % CI: 0.874-0.945), 0.765(95 % CI: 0.71-0.82), 0.657(95 % CI: 0.577-0.738), and 0.716 (95 % CI, 0.659-0.773) respectively. MOEWS had the highest AUCs in the discrimination of serious complications in hypertensive disorders, cardiovascular disease, obstetric hemorrhage and infection. For individual vital signs, maximum diastolic blood pressure (DBP), maximum systolic blood pressure (SBP), maximum respiratory rate (RR) and peripheral oxygen saturation (SPO2) demonstrated greater predictive ability. CONCLUSION: MOEWS is more accurate than ICNARC OEWS, MEOWS chart, and MEWT in predicting the deterioration of women. The prediction ability of DBP, SBP, RR and SPO2 are more reliable.

A Survey on Federated Recommendation Systems.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.

An efficient blue-violet phosphor: an advanced material designed for high-quality full-spectrum lighting.

A highly efficient phosphor with exceptional luminescence properties is crucial for achieving high-quality solid-state white-light illumination. Here, this paper presents a groundbreaking discovery, an innovative blue-violet emitting Ba1.31Sr3.69(BO3)3Cl:Ce3+ (BSBCl:Ce3+) phosphor designed with remarkable thermal stability and quantum efficiency for full spectrum white light-emitting diodes (WLEDs). By employing a high-temperature solid-phase method, we synthesized various BSBCl:xCe3+ phosphors with different Ce3+ doping concentrations. Remarkably, BSBCl:0.03Ce3+ displays a broad excitation band from 250 nm to 400 nm, rendering it compatible with commercial near-ultraviolet (UV) LED chips. Under 330 nm excitation, this phosphor emits blue light with an astonishing 88.2% internal quantum efficiency (IQE) and an impressive 60.9% external quantum efficiency (EQE). Notably, when employed in the temperature range of 298-473 K, the synthesized BSBCl:0.03Ce3+ phosphor exhibits exceptional color stability and thermal stability (I423 K/I298 K = 83%). Utilizing BSBCl:0.03Ce3+ as the blue-violet emitting component in the fabrication of WLED devices has demonstrated significant advancements in the color rendering index. These findings underscore the potential of BSBCl:Ce3+ phosphors for a wide range of applications in health-oriented indoor illumination.

High Output Power and High Quantum Efficiency in Novel NIR Phosphor MgAlGa0.7 B0.3 O4 :Cr3+ with Profound FWHM Variation.

There is strong demand for ultraefficient near-infrared (NIR) phosphors with adjustable emission properties for next-generation intelligent NIR light sources. Designing phosphors with large full-width at half-maximum (FWHM) variations is challenging. In this study, novel near-ultraviolet light-emitting diode (LED)-excited NIR phosphors, MgAlGa0.7 B0.3 O4 :Cr3+ (MAGBO:Cr3+ ), with three emission centers achieve ultra-narrowband (FWHM = 29 nm) to ultra-broadband (FWHM = 260 nm) emission with increasing Cr3+ concentration. Gaussian fitting and decay time analysis reveal the alteration in the FWHM, which is attributed to the energy transfer occurring between the three emission centers. The distinct thermal quenching behaviors of the three emission centers are revealed through the temperature-dependent decay times. The ultra-broadband NIR phosphor MAGBO:0.05Cr3+ exhibits high thermal stability (85%, 425 K) and exceptional external quantum efficiency of 68.5%. An NIR phosphor-converted LED (pc-LED) is fabricated using MAGBO:0.05Cr3+ phosphor, exhibiting a remarkable NIR output power of 136 mW at 600 mA in ultra-broadband NIR pc-LEDs. This study describes the preparation of highly efficient phosphors and provides a further understanding of the tunable FWHM, which is vital for high-performance NIR phosphors with versatile applications.

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

EAPR: explainable and augmented patient representation learning for disease prediction.

Patient representation learning aims to encode meaningful information about the patient's Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly improve the performance of disease prediction models. However, the inherent shortcomings of deep learning models, such as the need for massive amounts of labeled data and inexplicability, limit the performance of deep learning-based Patient representation learning methods to further improvements. In particular, learning robust patient representations is challenging when patient data is missing or insufficient. Although data augmentation techniques can tackle this deficiency, the complex data processing further weakens the inexplicability of patient representation learning models. To address the above challenges, this paper proposes an Explainable and Augmented Patient Representation Learning for disease prediction (EAPR). EAPR utilizes data augmentation controlled by confidence interval to enhance patient representation in the presence of limited patient data. Moreover, EAPR proposes to use two-stage gradient backpropagation to address the problem of unexplainable patient representation learning models due to the complex data enhancement process. The experimental results on real clinical data validate the effectiveness and explainability of the proposed approach.

Investigating whether exosomal miR-205-5p derived from tongue squamous cell carcinoma cells stimulates the angiogenic activity of HUVECs by targeting AMOT.

BACKGROUND: Although exosomal microRNAs (exo-miRNAs) regulate angiogenesis, they are not sufficient for the development of anti-vascular drugs for tongue squamous cell carcinoma (TSCC). miR-205-5p is an exo-miRNA that is highly expressed in the saliva of patients with oral SCC. OBJECTIVE: We aimed to clarify the role and molecular mechanism of exosomal miR-205-5p in regulating TSCC angiogenesis. METHODS: Effect of exosomes derived from TSCC cells on human umbilical vein endothelial cell (HUVEC) function was determined using the CCK-8, Transwell, Transwell-Matrigel, and Matrigel-based tube formation assays. Protein levels were detected by western blot. The binding between miR-205-5p and the 3'UTR of AMOT was verified using a luciferase reporter assay. RESULTS: Exosomal miR-205-5p (exo-miR-205-5p) promoted the proliferation, migration, and invasion of HUVECs, increased the number of tubes formed by HUVECs, and increased the vascular endothelial growth factor receptor 2 levels in HUVECs. Exo-miR-205-5p downregulated the AMOT level in HUVECs. Results of the luciferase reporter assay showed that miR-205-5p could bind to the 3'UTR of AMOT. AMOT overexpression blocked the effect of exo-miR-205-5p on HUVEC functions. CONCLUSION: Exo-miR-205-5p derived from TSCC regulates the angiogenic activity of HUVECs by targeting AMOT and might be a new molecular target for the development of anti-vascular drugs for TSCC.

Multicomponent Adversarial Domain Adaptation: A General Framework.

Domain adaptation (DA) aims to transfer knowledge from one source domain to another different but related target domain. The mainstream approach embeds adversarial learning into deep neural networks (DNNs) to either learn domain-invariant features to reduce the domain discrepancy or generate data to fill in the domain gap. However, these adversarial DA (ADA) approaches mainly consider the domain-level data distributions, while ignoring the differences among components contained in different domains. Therefore, components that are not related to the target domain are not filtered out. This can cause a negative transfer. In addition, it is difficult to make full use of the relevant components between the source and target domains to enhance DA. To address these limitations, we propose a general two-stage framework, named multicomponent ADA (MCADA). This framework trains the target model by first learning a domain-level model and then fine-tuning that model at the component-level. In particular, MCADA constructs a bipartite graph to find the most relevant component in the source domain for each component in the target domain. Since the nonrelevant components are filtered out for each target component, fine-tuning the domain-level model can enhance positive transfer. Extensive experiments on several real-world datasets demonstrate that MCADA has significant advantages over state-of-the-art methods.

Investigation of underlying links between nitrogen transformation and microorganisms' network modularity in the novel static aerobic composting of dairy manure by "stepwise verification interaction analysis".

Conventional composting is a viable method treating agricultural solid waste, and microorganisms and nitrogen transformation are the two major components of this proces. Unfortunately, conventional composting is time-consuming and laborious, and limited efforts have been made to mitigate these problems. Herein, a novel static aerobic composting technology (NSACT) was developed and employed for the composting of cow manure and rice straw mixtures. During the composting process, physicochemical parameters were analyzed to evaluate the quality of compost products, and microbial abundance dynamics were determined using high-throughput sequencing technique. The results showed that NSACT achieved compost maturity within 17 days as the thermophilic stage (≥55 °C) lasted for 11 days. GI, pH, and C/N were 98.71 %, 8.38, and 19.67 in the top layer, 92.32 %, 8.24, and 22.38 in the middle layer, 102.08 %, 8.33, and 19.95 in the bottom layer. These observations indicate compost products maturated and met the requirements of current legislation. Compared with fungi, bacterial communities dominated NSACT composting system. Based on the stepwise verification interaction analysis (SVIA), the novel combination utilization of multiple statistical analyses (Spearman, RDA/CCA, Network modularity, and Path analyses), bacterial genera Norank Anaerolineaceae (-0.9279*), norank Gemmatimonadetes (1.1959*), norank Acidobacteria (0.6137**) and unclassified Proteobacteria (-0.7998*), and fungi genera Myriococcum thermophilum (-0.0445), unclassified Sordariales (-0.0828*), unclassified Lasiosphaeriaceae (-0.4174**), and Coprinopsis calospora (-0.3453*) were the identified key microbial taxa affecting NH4+-N, NO3--N, TKN and C/N transformation in the NSACT composting matrix respectively. This work revealed that NSACT successfully managed cow manure-rice straw wastes and significantly shorten the composting period. Interestingly, most microorganisms observed in this composting matrix acted in a synergistic manner, promoting nitrogen transformation.

HealthNet: A Health Progression Network via Heterogeneous Medical Information Fusion.

Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predictive analysis of patient outcomes. However, most existing works on this task suffer from the following two limitations: 1) the diverse dependencies among heterogeneous medical entities are overlooked, which leads to the one-sided modeling of patients' status and 2) the extraction granularity of patient's health progression patterns is coarse, limiting the model's ability to accurately infer the patient's future status. To address these challenges, a pretrained Health progression network via heterogeneous medical information fusion, HealthNet, is proposed in this article. Specifically, a global heterogeneous graph in HealthNet is built to integrate heterogeneous medical entities and the dependencies among them. In addition, the proposed health progression network is designed to model hierarchical medical event sequences. By this method, the fine-grained health progression patterns of patients' health can be captured. The experimental results on real disease datasets demonstrate that HealthNet outperforms the state-of-the-art models for both diagnosis prediction task and mortality prediction task.

A new modified obstetric early warning score for prognostication of severe maternal morbidity.

BACKGROUND: Maternal mortality is still a major challenge for health systems, while severe maternal complications are the primary causes of maternal death. Our study aimed to determine whether severe maternal morbidity is effectively predicted by a newly proposed Modified Obstetric Early Warning Score (MOEWS) in the setting of an obstetric intensive care unit (ICU). METHODS: A retrospective study of pregnant women admitted in the ICU from August 2019 to August 2020 was conducted. MOEWS was calculated 24 h before and 24 h after admission in the ICU, and the highest score was taken as the final value. For women directly admitted from the emergency department, the worst value before admission was collected. The aggregate performance of MOEWS in predicting critical illness in pregnant women was evaluated and finally compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. RESULTS: A total of 352 pregnant women were enrolled; 290 women (82.4%) with severe maternal morbidity were identified and two of them died (0.6%). The MOEWSs of women with serious obstetric complications were significantly higher than those of women without serious obstetric complications [8(6, 10) vs. 4(2, 4.25), z = -10.347, P < 0.001]. MOEWSs of 24 h after ICU admission had higher sensitivity, specificity and AUROC than MOEWSs of 24 h before ICU admission. When combining the two MOEWSs, sensitivity of MOEWS was 99.3% (95% CI: 98-100), specificity 75.8% (95% CI: 63-86), positive predictive value (PPV) 95.1% (95% CI: 92-97) and negative predictive value (NPV) 95.9% (95% CI: 86-100). The areas under the receiver operator characteristic (ROC) curves of MOEWS were 0.92 (95% CI: 0.88-0.96) and 0.70 (95% CI: 0.63-0.76) of the APACHE II score. CONCLUSION: The newly proposed MOEWS has an excellent ability to identify critically ill women early and is more effective than APACHE II. It will be a valuable tool for discriminating severe maternal morbidity and ultimately improve maternal health.

The Use of Three-Column Enhanced Percutaneous Vertebroplasty to Treat Kummell's Disease.

Purpose: To describe the operative technique and clinical effects of three-column enhanced percutaneous vertebroplasty used to treat Kummell's disease. Methods: From April 2017 to April 2020, 39 patients with Kummell's disease were treated via three-column enhanced percutaneous vertebroplasty. There were 12 males and 27 females of average age 70.23 ± 7.41 years. The operative time, volume of bone cement injected, and intraoperative cement leakage were recorded. The patients were re-examined postoperatively. The VAS was used to evaluate low back pain and the ODI score to evaluate improvement in the quality-of-life. Results: All patients were successfully operated upon; the average operation time was 35.1±4.7 min and average volume of bone cement injected 4.5±0.92 mL. Five cases exhibited bone cement leakage during operation, two into the intervertebral disc and three into the anterior upper margin of the vertebral body. No leakage into the vertebral canal occurred. The average hospital stay was 2.50±0.86 days. The VAS score before operation was 7.47±0.24, but low back pain symptoms were significantly relieved after operation (P < 0.05). The VAS scores at 1 day and 1, 3, 6, and 12 months after operation were 2.91±0.09, 2.04±0.07, 1.59±0.05, 1.28±0.15, and 0.8±0.18, respectively. The preoperative ODI score was 72.97±1.45 and significantly decreased postoperatively (P < 0.05), being 30.08±1.79 at 1 day, and 25.35±0.94, 23.19±1.76, 20.49±0.65, and 20.05±0.58 at 1, 3, 6, and 12 months after operation respectively. Conclusion: Three-column enhanced percutaneous vertebroplasty effectively treats Kummell's disease. The surgical trauma is low, recovery rapid, and bone cement fixation firm, especially in patients with stage I and II disease.

Successful treatment of severe pityriasis rubra pilaris with secukinumab in a 3-year-old boy.

Pityriasis rubra pilaris (PRP) is a rare, scaly, keratotic inflammatory skin disease characterized by red scaly patches, keratosis papules, palmoplantar keratoderma and scaling of the scalp. In severe cases, ectropion of the eyelid may occur, and erythroderma may further develop. Recently, it has been reported that secukinumab, a monoclonal anti-interleukin-17A antibody, has certain efficacy in the treatment of PRP. Herein, we report a 3-year-old Chinese boy with severe Type III (classic juvenile) PRP who was successfully treated with secukinumab alone.

Broadband Near-Infrared-Emitting Phosphors with Suppressed Concentration Quenching in a Two-Dimensional Structure.

For inorganic luminescent materials with activators, the energy yield is usually observed to decrease with an increase in activator concentration, which is known as the concentration quenching effect. To inhibit this phenomenon, a common strategy is to increase the distance between activators. Most previous reports have focused on the three-dimensional crystal lattice, and there have been few reports about two-dimensional layered structure. Herein, we synthesized a novel Cr3+-activated near-infrared (NIR) phosphor Li2Sr2Al(PO4)3 (LSAPO) with layered structure, and in such a two-dimensional structure, we proved experimentally that the concentration quenching was suppressed. Under 460 nm excitation, LSAPO:Cr3+ gave a broad NIR emission band (700-1200 nm) centered at 823 nm with a full width at half-maximum (fwhm) of 178 nm and a broad absorption band, indicating its potential application in NIR spectroscopy. Moreover, by codoping Cr3+ and Yb3+ ions, we further widened the emission bandwidth to ∼230 nm of fwhm, the internal quantum efficiency increased from 54% to 61%, and the thermal stability was improved. The fabricated NIR device with a LSAPO:Cr3+,Yb3+ phosphor coupled with blue chips can be applied in night-vision technologies and medical fields.

Analysis of factors affecting the prognosis of transcatheter arterial chemoembolization for hepatitis B-related hepatocellular carcinoma.

OBJECTIVES: The purpose of this study was to investigate the prognostic factors for transcatheter arterial chemoembolization (TACE) for hepatitis B-related hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The variables that may affect overall survival (OS), such as age, gender, AFP, Child Pugh classification, body mass index, HBV-DNA, HbeAg, tumor number, tumor diameter, BCLC stage, embolization method, ablation therapy, and targeted therapy, were analyzed by single factor and many factor COX regression. In addition, predictive factors of OS were stratified and a Kaplan-Meier survival curve was drawn. RESULTS: Among the 136 patients, the median follow-up time was 14.5 months (range: 2-72 months). HCC patients with the tumor diameter <3 â€‹cm had the highest survival rate, followed by patients with a tumor diameter of 3-5 â€‹cm; the survival rate of patients with the tumor diameter (greater than 5 â€‹cm) was the lowest. Among the BCLC stages, stage A patients had the highest survival rate, followed by stage B and stage C patients, which had the lowest survival rate.The survival rate of Child Pugh grade A patients was higher than those with Child Pugh grade B. Compared with patients who did not undergo ablation treatment, the survival rate of patients with combined ablation treatment was relatively high. The survival rate of patients receiving drug-eluting beads transarterial chemoembolization (DEB-TACE) treatment was higher than those receiving conventional transarterial chemoembolization (cTACE) treatment. Additionally, repeated TACE treatment improved the OS rate of patients. These six factors were related to patient prognosis and the differences were statistically significant (P â€‹< â€‹0.05). CONCLUSIONS: Tumor diameter, BCLC stage, TACE repetition, and TACE combined with ablation were independent prognostic factors of OS.

Highly thermally stable Cr3+ and Yb3+ codoped Gd2GaSbO7 phosphors for broadband near-infrared applications.

Gd2GaSbO7:Cr3+,Yb3+ phosphors with efficient broadband NIR emission were prepared by a solid-state reaction. Under the excitation of 448 nm, the Gd2GaSbO7:Cr3+ (GGS:Cr3+) phosphor exhibits a broadband NIR emission band centered at approximately 770 nm with a full width at half maximum (FWHM) of 160 nm. In addition, Yb3+ codoping can distinctly improve the photoluminescence properties of the GGS:Cr3+ phosphor, leading to broadening of the FWHM and greatly enhancing the thermal stability of the phosphor. Moreover, the energy conversion process of Cr3+ → Yb3+ ions was analyzed in detail, demonstrating that the energy transfer mechanism conformed to electric dipole-dipole interaction. The NIR pc-LEDs assembled with the GGS:Cr3+ phosphor and blue LED chips possessed a maximum NIR output power of ∼21 mW at 100 mA driving current, indicating promising applications of the synthesized phosphor in NIR pc-LEDs.

Novel SrGd2Al2O7:Mn4+, Nd3+, and Yb3+ phosphors for c-Si solar cells.

Novel single-doped and codoped SrGd2Al2O7-based (SGA) phosphors with tunable emission were synthesized via the solid-state reaction approach. The optimal SGA:0.0008Mn4+ phosphor presents an emission band peaking at 709 nm and shows great red luminescence properties. With the incorporation of Nd3+/Yb3+ into SGA:0.0008Mn4+, an efficient energy transfer Mn4+→ Nd3+/Yb3+ was observed. When Nd3+ and Yb3+ were codoped into SGA:0.0008Mn4+, an energy transfer mechanism from Mn4+ to Nd3+ to Yb3+ was found on the basis of the energy transfer mediation of Nd3+ connecting the Mn4+ and Yb3+ luminescent centers. It results in a strong near-infrared emission in the spectral region of high response of c-Si solar cells, which suggests a potential approach to improve the energy conversion efficiency of c-Si solar cells. The findings offer a novel route to design new down-conversion luminescent materials for the c-Si solar cells.