Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Pressure-Induced Defects and Reduced Size Endow TiO2 with High Capacity over 20 000 Cycles and Excellent Fast-Charging Performance in Sodium Ion Batteries.

Anatase TiO2 as sodium-ion-battery anode has attracted increased attention because of its low volume change and good safety. However, low capacity and poor rate performance caused by low electrical conductivity and slow ion diffusion greatly impede its practical applications. Here, a bi-solvent enhanced pressure strategy that induces defects (oxygen vacancies) into TiO2 via N doping and reduces its size by using mutual-solvent ethanol and dopant dimethylformamide as pressure-increased reagent of tetrabutyl orthotitanate tetramer is proposed to fabricate N-doped TiO2 /C nanocomposites. The induced defects can increase ion storage sites, improve electrical conductivity, and decrease bandgap and ion diffuse energy barrier of TiO2 . The size reduction increases contact interfaces between TiO2 and C and shortens ion diffuse distance, thus increasing extra ion storage sites and boosting ion diffusion rate of TiO2 . The N-doped TiO2 possesses highly stable crystal structure with a slightly increase of 0.86% in crystal lattice spacing and 3.2% in particle size after fully sodiation. Consequently, as a sodium-ion battery anode, the nanocomposite delivers high capacity and superior rate capability along with ultralong cycling life. This work proposes a novel pressure-induced synthesis strategy that provides unique guidance for designing TiO2 -based anode materials with high capacity and excellent fast-charging capability.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards.

BACKGROUND: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. RESULTS: Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. CONCLUSION: UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry.

Highly Efficient Aligned Ion-Conducting Network and Interface Chemistries for Depolarized All-Solid-State Lithium Metal Batteries.

Improving the long-term cycling stability and energy density of all-solid-state lithium (Li)-metal batteries (ASSLMBs) at room temperature is a severe challenge because of the notorious solid-solid interfacial contact loss and sluggish ion transport. Solid electrolytes are generally studied as two-dimensional (2D) structures with planar interfaces, showing limited interfacial contact and further resulting in unstable Li/electrolyte and cathode/electrolyte interfaces. Herein, three-dimensional (3D) architecturally designed composite solid electrolytes are developed with independently controlled structural factors using 3D printing processing and post-curing treatment. Multiple-type electrolyte films with vertical-aligned micro-pillar (p-3DSE) and spiral (s-3DSE) structures are rationally designed and developed, which can be employed for both Li metal anode and cathode in terms of accelerating the Li+ transport within electrodes and reinforcing the interfacial adhesion. The printed p-3DSE delivers robust long-term cycle life of up to 2600 cycles and a high critical current density of 1.92 mA cm-2. The optimized electrolyte structure could lead to ASSLMBs with a superior full-cell areal capacity of 2.75 mAh cm-2 (LFP) and 3.92 mAh cm-2 (NCM811). This unique design provides enhancements for both anode and cathode electrodes, thereby alleviating interfacial degradation induced by dendrite growth and contact loss. The approach in this study opens a new design strategy for advanced composite solid polymer electrolytes in ASSLMBs operating under high rates/capacities and room temperature.

RPIOSL: construction of the radiation transfer model for rice leaves.

The radiative transfer model of vegetation leaves simulates the transmission mechanism of light inside the vegetation and simulates the reflectivity of blades according to the change law of different components in the process of plant growth. Based on the PIOSL model, this paper combines PIOSL with the structure of rice leaves to construct a radiation transfer model for rice leaves. The parameters of each layer of the RPIOSL model are determined by the Non-dominated Sorting Genetic Algorithm-III. (NSGA-III.) algorithm. The reflectance spectra of 218 rice leaf samples in different periods were simulated using the RPIOSL model. The results show that the mean (RMSE) between the simulated and measured spectra of the constructed RPIOSL model is 0.1074, which is 0.0191 lower than that of the PROSPECT model. Among them, the spectral simulation effect of RPIOSL model in yellow and red light band is the best, and the RMSE at tillering period, jointing period, heading period and grouting period are 0.0584, 0.0576, 0.0724 and 0.0820, respectively. Therefore, the establishment of the RPIOSL model can accurately describe the interaction mechanism between light, which is of great significance for the rapid acquisition of rice growth information and accurate crop management.

Estimating the rice nitrogen nutrition index based on hyperspectral transform technology.

Background and objective: The rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance. Methods: This study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables. Results: 1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1). Conclusion: The logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.

Research on weed identification method in rice fields based on UAV remote sensing.

Rice is the world's most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVI NIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVI NIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVI NIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVI NIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

Effect of Electrical Stimulation Followed by Exercises in Postnatal Diastasis Recti Abdominis via MMP2 Gene Expression.

Since abdominal muscle training is one of the most important ways to treat rectal diastasis, it is necessary to design and provide appropriate exercises to treat this problem. One of the complementary methods to achieve higher intensity training is the use of electrical muscle stimulation along with voluntary activity, which causes maximum recall in muscle units. Therefore, in the current study, the effect of electrical stimulation followed by exercises was evaluated in postnatal diastasis recti abdominis via MMP2 gene expression. For this purpose, we studied on thirty-two women who had rectal diastasis for six months and were referred to a physiotherapy clinic by a gynecologist. They were divided into the control group (n=16) and the intervention group (n=16). The distance between the two blocks of the rectus abdominis muscle and the thickness of the abdominal muscles at rest was determined by ultrasound. In the intervention group, electrical stimulation and strengthening exercises of oblique muscles were performed for six weeks. The control group did not perform any specific exercises. After six weeks, another ultrasound was performed. The expression of the MMP2 gene was measured by the real-time PCR method. Comparison of the distance between the two blocks of rectus abdominis muscle (above the umbilicus and below the umbilicus) before and after six weeks showed that in the experimental group after intervention in both areas had a significant decrease (above the umbilicus = 0.001 and below the umbilicus P = 0.03), while this distance in the control group did not decrease significantly (p >0.05). Also, in the upper part of the umbilicus, the distance between the two rectus abdominis muscle blocks in two groups after the intervention showed a significant difference (p = 0.04). Evaluation of MMP2 gene expression showed that there was no significant difference between the two groups before the intervention (p >0.05). However, after the intervention, the expression of this gene decreased significantly in the intervention group (p = 0.007). In general, the present study results showed that electrical stimulation of abdominal muscles with strengthening exercises of internal and external oblique muscles could reduce rectal diastasis and increase the thickness of these muscles in people with rectal diastasis.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

Droplet deposition and pest control efficacy on pine trees from aerial application.

BACKGROUND: Aerial application, a spraying method with aircraft as the application platform, is one of the most important methods of pest control in pine forests. This work aimed to investigate the movement and distribution patterns of droplets and the pest control effectiveness of helicopter spraying on Masson pine (MP) and Korean pine (KP) trees. In particular, three nozzle types (CP02, CP03 and CP04) were evaluated and compared at the same spray volume rate (15 L ha-1 ) in both wind tunnel and field trials. RESULTS: The CP04 nozzle with a larger orifice size showed significantly better deposition and penetration, but the precision of flight operation parameters must be strictly controlled when using this type of nozzle. The majority (≥48%) of the droplets produced by the three nozzle types were concentrated in the range of <200 µm, and the spray performance of each nozzle type was more stable in the range 201-300 µm. The final pest correction control rate (CR) of MP was in the range 59.24-95.74% and that of KP was 45.41-54.39% by helicopter spraying. CONCLUSION: This study confirmed that it is necessary to select the appropriate nozzle type and ensure accurate flight parameters for aerial application in mountain pine forests. Meanwhile, it also confirms the importance of the timing of operation which can lead to incorrect implementation, resulting in poor control effectiveness. © 2022 Society of Chemical Industry.

Whole-genome sequencing to characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in Yichang city of China.

Objective: The burden of both general and drug-resistant tuberculosis in rural areas is higher than that in urban areas in China. To characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in rural China, we used whole genome sequencing to analyze clinical strains collected from patients in two counties of Yichang for three consecutive years. Methods: From 2018 to 2020, sputum samples were collected for cultures from patients with suspected tuberculosis in Yidu and Zigui county, and DNA was extracted from the positive strains for genome sequencing. The online SAM-TB platform was used to identify the genotypes and drug resistance-related mutations of each strain, establish a phylogenetic tree, and calculated the genetic distances between pairwise strains. Twelve single nucleotide polymorphisms (SNPs) were used as thresholds to identify transmission clusters. The risk of related factors was estimated by univariable and multivariable logistic regression. Results: A total of 161 out of the collected 231 positive strains were enrolled for analysis, excluding non-tuberculous mycobacterium and duplicate strains from the same patient. These strains belonged to Lineage 2 (92, 57.1%) and Lineage 4 (69, 42.9%), respectively. A total of 49 (30.4%) strains were detected with known drug resistance-related mutations, including 6 (3.7%) multidrug-resistant-TB (MDR-TB) strains and 11 (6.8%) RIF-resistant INH-susceptible TB (Rr-TB) strains. Six of the MDR/Rr-TB (35.3%) were also resistant to fluoroquinolones, which made them pre-extensively drug-resistant TB (pre-XDR-TB). There were another seven strains with mono-resistance to fluoroquinolones and one strain with resistance to both INH and fluoroquinolones, making the overall rate of fluoroquinolones resistance 8.7% (14/161). A total of 50 strains (31.1%) were identified as transmission clusters. Patients under 45 years old (adjusted odds ratio 3.46 [95% confidential intervals 1.28-9.35]), treatment-naive patients (6.14 [1.39-27.07]) and patients infected by lineage 4 strains (2.22 [1.00-4.91]) had a higher risk of transmission. Conclusion: The drug resistance of tuberculosis in rural China, especially to the second-line drug fluoroquinolones, is relatively serious. The standardized treatment for patients and the clinical use of fluoroquinolones warrant attention. At the same time, the recent transmission risk of tuberculosis is high, and rapid diagnosis and treatment management at the primary care needs to be strengthened.

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential.

To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.

Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing.

Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an inversion model that can predict japonica rice chlorophyll content by using hyperspectral image of rice canopy collected with unmanned aerial vehicle (UAV). UAV-based hyperspectral remote sensing can provide timely and cost-effective monitoring of chlorophyll content over a large region. The study was based on hyperspectral data collected at the Shenyang Agricultural College Academician Japonica Rice Experimental Base in 2018 and 2019. In order to extract the salient information embedded in the high-dimensional hyperspectral data, we first perform dimension reduction by using a successive projection algorithm (SPA). The SPA extracts the characteristic hyperspectral bands that are used as input to the inversion model. The characteristic bands extracted by SPA are 410 nm, 481 nm, 533 nm, 702 nm, and 798 nm, respectively. The inversion model is developed by using an extreme learning machine (ELM), the parameters of which are optimized by using particle swarm optimization (PSO). The PSO-ELM algorithm can accurately model the nonlinear relationship between hyperspectral data and chlorophyll content. The model achieves a coefficient of determination R2 = 0.791 and a root mean square error of RMSE = 8.215 mg/L. The model exhibits good predictive ability and can provide data support and model reference for research on nutrient diagnosis of japonica rice.

Shock Index on Admission Is Associated with Coronary Slow/No Reflow in Patients with Acute Myocardial Infarction Undergoing Emergent Percutaneous Coronary Intervention.

OBJECTIVE: Coronary slow/no reflow is not rare after successfully undergoing primary percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI), and shock index (SI) is an important factor for adverse cardiovascular prognosis. In this study, we are to explore whether SI is associated with coronary slow/no reflow in patients with AMI following primary PCI. METHODS: A total of 153 consecutive AMI patients undergoing primary PCI within 24 hours of symptom onset were included in this study. The participants were divided into normal flow group (n=124) and slow/no reflow group (n=29) according to cineangiograms recorded during the period of PCI. Cardiovascular risk factors, hematologic parameters, preoperative management of antithrombotic therapy, and baseline angiography were collected. RESULTS: SI, plasma glucose, white blood cells (WBC) and neutrophil count, neutrophil to lymphocyte ratio (PLR), high sensitivity C-reactive protein (hs-CRP), probrain natriuretic peptide (pro-BNP), and Killip classification on admission and thrombus burden on initial angiography were significantly different between patients with and without slow/no reflow. Multivariate analysis revealed that SI≥0.66, thrombus burden, and plasma glucose on admission were independent predictors for coronary slow/no reflow. Preoperative management of tirofiban therapy improves initial thrombolysis in myocardial infarction (TIMI). However, it has no effect on prognosis of slow/no reflow. CONCLUSION: Our findings demonstrated that slow/no reflow in patients with AMI following primary PCI was more likely associated with SI≥0.66, thrombus burden, and plasma glucose on admission. SI as a predictor for coronary slow/no reflow should be further confirmed in the following more large-scale and prospective studies. The clinical registration number is ChiCTR1900024447.

RETRACTED: Ginsenoside Rh2 inhibits proliferation but promotes apoptosis and autophagy by down-regulating microRNA-638 in human retinoblastoma cells.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The journal was initially contacted by the corresponding author to report that the article was submitted without the consent of an author. Given the comments of Dr Elisabeth Bik regarding this article "… the Western blot bands in all 400+ papers are all very regularly spaced and have a smooth appearance in the shape of a dumbbell or tadpole, without any of the usual smudges or stains. All bands are placed on similar looking backgrounds, suggesting they were copy/pasted from other sources, or computer generated ", the journal requested the authors to provide the raw data. However, the authors were not able to fulfil this request and therefore the Editor-in-Chief decided to retract the article.

Association of rs11801299 and rs1380576 polymorphisms at MDM4 with risk, clinicopathological features and prognosis in patients with retinoblastoma.

BACKGROUND: rs11801299 and rs1380576, two novel polymorphisms in MDM4 gene, have been investigated in several different cancer types. However, the role of these two polymorphisms in retinoblastoma (RB) remains unclear. METHODS: A total of 126 patients with primary RB and 148 age-/gender-matched controls were included in this retrospective study. The frequency of rs11801299 and rs1380576 were determined between RB patients and controls. The association of these two polymorphisms with clinicopathological characteristics, prognosis were further evaluated. RESULTS: AA genotype at rs11801299 was significantly associated with an increased risk of developing RB (OR = 2.06, 95%CI 1.09-3.90). The possibility of developing RB was also significantly increased in individuals with A allele at rs11801299 (OR = 1.49, 95%CI 1.06-2.08). RB patients carrying AA genotype and A allele at rs11801299 were more likely to have tumor invasion and poor differentiation. As for rs1380576, a significantly lower risk of developing RB was observed in patients with G allele (CG + GG) compared with wild-type CC genotype (OR = 0.59, 95%CI 0.36-3.95). RB patients with GG genotype or G allele had a lower risk of developing highly aggressive cancer. Kaplan-Meier curves and log-rank results revealed that RB patients carrying AA genotype or A allele (AA + GA) at rs11801299 had significantly poorer prognosis. Multivariate COX analysis showed that the rs11801299 G allele was associated with decreased survival but was not an independent prognostic factor. CONCLUSION: rs11801299 was significantly associated with RB risk, pathological differentiation, tumor aggressiveness and poor prognosis.

RETRACTED: Knockdown of lncRNA-H19 inhibits cell viability, migration and invasion while promotes apoptosis via microRNA-143/RUNX2 axis in retinoblastoma.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Given the comments of Dr Elisabeth Bik regarding this article "In almost all papers, Western blot panels within the same figure, and across figures and papers, appear to share the same background, while the bands are regularly spaced, all have similar rounded edges without the usual smudges and specks, and with some bands showing a recognizable "jumping sardine" shape", the journal requested the authors to provide the raw data. However, the authors were not able to fulfil this request and therefore the Editor-in-Chief decided to retract the article.

Upregulation of BCL2 19 kD Protein-Interacting Protein 3 (BNIP3) is Predictive of Unfavorable Prognosis in Uveal Melanoma.

BACKGROUND BCL2 19 kD protein-interacting protein 3 (BNIP3) is a BH3-containing protein of the BCL-2 family; it can regulate cell death, autophagy, and cytoprotection. The upregulation of BNIP3 has been reported to relate to progression and poor prognosis in different cancer types. However, the clinical significance of BNIP3 in uveal melanoma (UM) is still unknown. MATERIAL AND METHODS In our study, 47 patients with UM were enrolled; the expression of BNIP3 was detected with immunohistochemistry. According to BNIP3 immunohistochemical scores, the patients were divided into BNIP3 high- and low-expression subgroups. The correlation between the expression of BNIP3 and clinicopathological factors was evaluated with Fisher's test; the associations with survival rates were analyzed with log-rank test. The independent prognostic factors were identified with the Cox-regression model. RESULTS BNIP3 was mainly localized in the cytoplasm, and high expression of BNIP3 accounted for 31.9% (15/47) of the patients in our study. High expression of BNIP3 was demonstrated to be significantly associated with more pigment (P=0.018) and deeper scleral invasion (P=0.013). High expression of BNIP3 was also correlated with lower overall survival rate (P=0.006). Multivariate analysis confirmed positive ciliary body involvement and lymphatic infiltration as independent prognostic factors. CONCLUSIONS High expression of BNIP3 was significantly associated with poor prognosis of patients with UM, indicating that BNIP3 detection could help stratify high-risk patients and identify new therapies targeting BNIP3 as a promising approach to treat UM.