Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia.

AIM: To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms. METHODS: A dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model. RESULTS: According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF. CONCLUSIONS: The present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center study.

OBJECTIVE: This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: A total of 519 children with COVID-19 were assessed to develop predictive models using machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR). The performance of the models was assessed using area under the receiver operating characteristic curve (AUC) values. Importance matrix plot and SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and to show the visualization results. The nomogram and clinical impact curve were used to validate the final model. RESULTS: Two hundred and seventeen children with COVID-19 had seizures. According to the AUC, the RF model performed the best. Based on the SHAP values, the top three most important variables in the RF model were neutrophil percentage, cough and fever duration. The nomogram and clinical impact curve also verified that the RF model possessed significant predictive value. CONCLUSIONS: Our research indicates that the RF model demonstrates excellent performance in predicting seizures, and our novel nomogram can facilitate clinical decision-making and potentially offer benefit for clinicians to prevent and treat seizures in children with COVID-19.

Life makes cities greener: The impact of dual-policy of China in urban transformation on residents' green lifestyles.

Promoting the formation of the green lifestyle (GL) is a crucial step in achieving comprehensive green transformation of urban economic and social development. The widespread adoption of GL is influenced by various environmental regulations. Previous research mainly focused on the impact of individual policies on GL from the single policy perspective. The mechanisms of the combined effects of policies have not been thoroughly explored, particularly the contributions of each policy during periods of overlap. This paper takes the dual-policy of the New-type Urbanization Policy (NUP) and Smart City Policy (SCP) in China as an example. It employs panel data collected from 271 cities in China during 2007-2019 and establishes a multi-period difference-in-difference model to identify the combined effects of the dual-policy on residents' GL. Additionally, the Shapley value decomposition method is utilized to identify the contribution magnitude of each policy when they act simultaneously. The following conclusions are yielded. Firstly, the combined effects of dual-policy are more effective than a single policy in influencing residents' GL. Secondly, the Shapley value decomposition method reveals that when both policies are simultaneously implemented, SCP contributes a greater weight compared to NUP. Thirdly, the dual-policy can promote residents' adoption of GL through mechanisms such as green technological innovation, public participation in environmental protection, and the agglomeration of tertiary industries. Fourthly, the impact of dual-policy on residents' GL varies across different types and sizes of cities. This study attempts to unseal the "black box" of how the dual-policy influences residents' GL during the green transformation of cities in China, providing theoretical references for relevant urban policies in other countries and contributing to Chinese solutions and experience to global urban green development.

LincRNA ZNF529-AS1 inhibits hepatocellular carcinoma via FBXO31 and predicts the prognosis of hepatocellular carcinoma patients.

BACKGROUND: Invasion and metastasis of hepatocellular carcinoma (HCC) is still an important reason for poor prognosis. LincRNA ZNF529-AS1 is a recently identified tumour-associated molecule that is differentially expressed in a variety of tumours, but its role in HCC is still unclear. This study investigated the expression and function of ZNF529-AS1 in HCC and explored the prognostic significance of ZNF529-AS1 in HCC. METHODS: Based on HCC information in TCGA and other databases, the relationship between the expression of ZNF529-AS1 and clinicopathological characteristics of HCC was analysed by the Wilcoxon signed-rank test and logistic regression. The relationship between ZNF529-AS1 and HCC prognosis was evaluated by Kaplan‒Meier and Cox regression analyses. The cellular function and signalling pathways involved in ZNF529-AS1 were analysed by GO and KEGG enrichment analysis. The relationship between ZNF529-AS1 and immunological signatures in the HCC tumour microenvironment was analysed by the ssGSEA algorithm and CIBERSORT algorithm. HCC cell invasion and migration were investigated by the Transwell assay. Gene and protein expression were detected by PCR and western blot analysis, respectively. RESULTS: ZNF529-AS1 was differentially expressed in various types of tumours and was highly expressed in HCC. The expression of ZNF529-AS1 was closely correlated with the age, sex, T stage, M stage and pathological grade of HCC patients. Univariate and multivariate analyses showed that ZNF529-AS1 was significantly associated with poor prognosis of HCC patients and could be an independent prognostic indicator of HCC. Immunological analysis showed that the expression of ZNF529-AS1 was correlated with the abundance and immune function of various immune cells. Knockdown of ZNF529-AS1 in HCC cells inhibited cell invasion and migration and inhibited the expression of FBXO31. CONCLUSION: ZNF529-AS1 could be a new prognostic marker for HCC. FBXO31 may be the downstream target of ZNF529-AS1 in HCC.

Quantum-Squeezing-Induced Point-Gap Topology and Skin Effect.

We theoretically predict the squeezing-induced point-gap topology together with a symmetry-protected Z_{2} "skin effect" in a one-dimensional (1D) quadratic-bosonic system. Protected by a time-reversal symmetry, such a topology is associated with a novel Z_{2} invariant (similar to quantum spin-Hall insulators), which is fully capable of characterizing the occurrence of the Z_{2} skin effect. Focusing on zero energy, the parameter regime of this skin effect in the phase diagram just corresponds to a "real- and point-gap coexisting topological phase." Moreover, this phase associated with the symmetry-protected Z_{2} skin effect is experimentally observable by detecting the steady-state power spectral density. Our Letter is of fundamental interest in enriching non-Bloch topological physics by introducing quantum squeezing and has potential applications for the engineering of symmetry-protected sensors based on the Z_{2} skin effect.

Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children.

BACKGROUND: Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis. PURPOSE: To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM. STUDY TYPE: Retrospective. POPULATION: A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination. FIELD STRENGTH/SEQUENCE: A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging. ASSESSMENT: Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis. STATISTICAL TESTS: Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models. RESULTS: Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05). DATA CONCLUSION: Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Clinical presentations and outcomes of pancreaticobiliary maljunction in different pediatric age groups.

BACKGROUND: Pancreaticobiliary maljunction (PBM) is a congenital defect, with risk of developing various pancreaticobiliary and hepatic complications. The presentations of PBM in children and adults are believed to be different, but studies on PBM children of different age groups are limited. This study was to evaluate clinicopathologic characteristics and outcomes in PBM children of different ages. METHODS: A total of 166 pediatric patients with PBM were reviewed retrospectively. Clinicopathological, imaging, laboratory, surgical, and follow-up data were collected and analyzed. The patients were divided into three age groups, namely, group A (< 1 year, n = 31), group B (1-3 years, n = 63), and group C (> 3 years, n = 72). RESULTS: The major clinical manifestation was jaundice in group A and abdominal pain and vomiting in groups B and C. Acute pancreatitis was more often seen in group C than group A. The length of common channel was significantly longer in group C than group A, while the maximum diameter of common bile duct in group C was smaller than that in group A. Cholangitis and cholecystitis were more commonly performed in groups B and C, while hepatic fibrosis in group A. Whether preoperatively or postoperatively, group C was more likely to have elevated serum amylase, while groups A and B were more likely to present with abnormal liver function indicators, including the increase of aspartate transaminase, alanine transaminase, and gamma-glutamyl transpeptidase. CONCLUSION: Presentation of PBM varies among different pediatric age groups, thus suggesting that targeted management should be carried out according to these differences.

Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children.

BACKGROUND: To identify radiomic features that can predict the pathological type of neuroblastic tumor in children. METHODS: Data on neuroblastic tumors in 104 children were retrospectively analyzed. There were 14 cases of ganglioneuroma, 24 cases of ganglioneuroblastoma, and 65 cases of neuroblastoma. Stratified sampling was used to randomly allocate the cases into the training and validation sets in a ratio of 3:1. The maximum relevance-minimum redundancy algorithm was used to identify the top 10 of two clinical features and 851 radiomic features in portal venous-phase contrast-enhanced computed tomography images. Least absolute shrinkage and selection operator regression was used to classify tumors in two binary steps: first as ganglioneuroma compared to the other two types, then as ganglioneuroblastoma compared to neuroblastoma. RESULTS: Based on 10 clinical-radiomic features, the classifier identified ganglioneuroma compared to the other two tumor types in the validation dataset with sensitivity of 100.0%, specificity of 81.8%, and an area under the receiver operating characteristic curve (AUC) of 0.875. The classifier identified ganglioneuroblastoma versus neuroblastoma with a sensitivity of 83.3%, a specificity of 87.5%, and an AUC of 0.854. The overall accuracy of the classifier across all three types of tumors was 80.8%. CONCLUSION: Radiomic features can help predict the pathological type of neuroblastic tumors in children.

Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction.

PURPOSE: To develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM). METHODS: We screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC). RESULTS: A total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count. CONCLUSIONS: Machine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.

Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.

PURPOSE: To develop machine learning (ML) models to predict the surgical risk of children with pancreaticobiliary maljunction (PBM) and biliary dilatation. METHODS: The subjects of this study were 157 pediatric patients who underwent surgery for PBM with biliary dilatation between January, 2015 and August, 2022. Using preoperative data, four ML models were developed, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost). The performance of each model was assessed via the area under the receiver operator characteristic curve (AUC). Model interpretations were generated by Shapley Additive Explanations. A nomogram was used to validate the best-performing model. RESULTS: Sixty-eight patients (43.3%) were classified as the high-risk surgery group. The XGBoost model (AUC = 0.822) outperformed the LR (AUC = 0.798), RF (AUC = 0.802) and SVC (AUC = 0.804) models. In all four models, enhancement of the choledochal cystic wall and an abnormal position of the right hepatic artery were the two most important features. Moreover, the diameter of the choledochal cyst, bile duct variation, and serum amylase were selected as key predictive factors by all four models. CONCLUSIONS: Using preoperative data, the ML models, especially XGBoost, have the potential to predict the surgical risk of children with PBM and biliary dilatation. The nomogram may provide surgeons early warning to avoid intraoperative iatrogenic injury.

Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model.

PURPOSE: This study aimed to develop a prediction model to identify risk factors for post-operative acute pancreatitis (POAP) in children with pancreaticobiliary maljunction (PBM) by pre-operative analysis of patient variables. METHODS: Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were established using the prospectively collected databases of patients with PBM undergoing surgery which was reviewed in the period comprised between August 2015 and August 2022, at the Children's Hospital of Soochow University. Primarily, the area beneath the receiver-operating curves (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The model was finally validated using the nomogram and clinical impact curve. RESULTS: In total, 111 children with PBM met the inclusion criteria, and 21 children suffered POAP. In the validation dataset, LR models showed the highest performance. The risk nomogram and clinical effect curve demonstrated that the LR model was highly predictive. CONCLUSION: The prediction model based on the LR with a nomogram could be used to predict the risk of POAP in patients with PBM. Protein plugs, age, white blood cell count, and common bile duct diameter were the most relevant contributing factors to the models.

Highly Efficient Multi-Resonance Thermally Activated Delayed Fluorescence Material with a Narrow Full Width at Half-Maximum of 0.14 eV.

Multi-resonance thermally activated delayed fluorescence (MR-TADF) material, which possesses the ability to achieve narrowband emission in organic light-emitting diodes (OLEDs), is of significant importance for wide color gamut and high-resolution display applications. To date, MR-TADF material with narrow full width at half-maximum (FWHM) below 0.14 eV still remains a great challenge. Herein, through peripheral protection of MR framework by phenyl derivatives, four efficient narrowband MR-TADF emitters are successfully designed and synthesized. The introduction of peripheral phenyl-based moieties via a single bond significantly suppresses the high-frequency stretching vibrations and reduces the reorganization energies, accordingly deriving the resulting molecules with small FWMH values around 20 nm/0.11 eV and fast radiative decay rates exceeding 108 s-1 . The corresponding green OLED based on TPh-BN realizes excellent performance with the maximum external quantum efficiency (EQE) up to 28.9% without utilizing any sensitizing host and a relatively narrow FWHM of 0.14 eV (28 nm), which is smaller than the reported green MR-TADF molecules in current literatures. Especially, the devices show significantly reduced efficiency roll-off and relatively long operational lifetimes among the sensitizer-free MR-TADF devices. These results clearly indicate the promise of this design strategy for highly efficient OLEDs with ultra-high color purity.

Deefgea salmonis sp. nov., isolated from gills of rainbow trout (Oncorhynchus mykiss).

A Gram-stain-negative, milky white, aerobic, rod-shaped bacterium named strain H3-26T was isolated from gills of Oncorhynchus mykiss in Lhasa, Tibet Autonomous Region, PR China. Strain H3-26T grew at 4-30 °C and pH 5.0-11.0 (optimum, 25 °C and pH 7.0) with 0-1% (w/v) NaCl (optimum, 0%). The 16S rRNA gene sequence of strain H3-26T showed the highest similarity to Deefgea rivuli WB 3.4-79T (98.42%), followed by Deefgea chitinilytica Nsw-4T (96.91%). Phylogenetic analysis based on 16S rRNA genes indicated that strain H3-26T was a new member of the genus Deefgea. The digital DNA-DNA hybridization and average nucleotide identity values between the genome sequence of strain H3-26T and Deefgea spp. were 21.2-21.9% and 76.3-77.4%, respectively. The genomic DNA G+C content of strain H3-26T was 48.74%. The predominant fatty acids were C12:0, C14:0, C16:0 and C16:1 ω7c. Based on phenotypic, phylogenetic, and genotypic data, strain H3-26T is considered to represent a novel species of the genus Deefgea, for which the name Deefgea salmonis sp. nov. is proposed. The type strain is H3-26T (= JCM 35050T = CICC 25103T).

Toxicity, biodegradation of moxifloxacin and gatifloxacin on Chlamydomonas reinhardtii and their metabolic fate.

The novel fourth-generation fluoroquinolones (FQs) were developed to improve the antimicrobial activity and their utilization has rapidly increased in recent years. However, knowledge of the ecotoxicity and microalgae-mediated biodegradation of these novel FQs is limited. In this research, the toxic effects of moxifloxacin (MOX) and gatifloxacin (GAT) on Chlamydomonas reinhardtii as well as their biodegradation and metabolic fate were investigated. The results showed that the toxicity of MOX to C. reinhardtii was higher than that of GAT, and increased with culture time. Chlorophyll fluorescence and pigment content analyses suggested that the decrease in photosynthetic efficiency was primarily caused by the inhibition of electron transport after QA in PSII complex. These FQs induced oxidative damage in cells, and the antioxidation mechanisms of C. reinhardtii were analyzed. The maximum MOX removal of 77.67% by C. reinhardtii was achieved at 1 mg/L MOX, whereas the maximum GAT removal of 34.04% was attained at 20 mg/L GAT. The different hydrophilicity and lipophilicity of these FQs resulted in distinct findings in biodegradation experiments. Identification of the transformation products suggested that the likely biodegradation pathways of FQs by C. reinhardtii were hydroxylation, demethylation, and ring cleavage.

Predictors of Bronchopulmonary Dysplasia in 625 Neonates with Respiratory Distress Syndrome.

OBJECTIVE: This study was designed to investigate the predictors of bronchopulmonary dysplasia in neonates with respiratory distress syndrome. METHODS: This was a single-center retrospective cohort study conducted between 1 January 2015 and 31 December 2020. A total of 625 neonates with respiratory distress syndrome (RDS) were enrolled. Demographic data, clinical presentations, complications and related treatment information were collected and analyzed. We used bivariate and multivariate logistic-regression analyses to determine significant predictors of bronchopulmonary dysplasia (BPD) in RDS neonates. RESULTS: In these 625 neonates, 102 (16.3%) of them developed BPD. Bivariate analysis and multivariate logistic-regression analyses revealed that birthweight, gestational age under 32 weeks, duration of oxygen therapy over 10 days, asphyxia, patent ductus arteriosus, transfusion of red blood cells (packed red blood cells) and surfactant use were significantly associated with the development of BPD. CONCLUSION: Birthweight, gestational age <32 weeks, total duration of oxygen therapy >10 days, asphyxia, patent ductus arteriosus, need for red blood cell infusion, and the use of pulmonary surfactant were important predictors of BPD in neonates with RDS.

How does the ecological compensation mechanism adjust the industrial structure? Evidence from China.

The ecological compensation (EC) mechanism is an innovative institutional arrangement which can effectively realize the coordinated development of social-economic growth and ecological protection. The current research on the evaluation of the implementation effect of EC mechanism mainly focuses on its environmental performance, how the EC mechanism guides the economically underdeveloped areas in the upper reaches of the basin to embark on the road of high-quality economic development, especially the mechanism of how EC triggers the optimization and upgrading of industrial structure, it is a topic worthy of in-depth discussion. This study takes China's first cross-provincial horizontal EC mechanism pilot (Xin'an River Basin EC) as the research object and regards it as a quasi-natural experiment. This paper selects the annual panel data of the 11 cities involved from 2009 to 2019 and builds a DID model to study the mechanism of EC affecting industrial structure adjustment systematically. The results show that the EC mechanism mainly promotes the adjustment of the industrial structure in the pilot area through two paths, this is achieved by triggering the transfer of backward or highly polluting industries and promoting the upgrading of the industrial structure. At the same time, technological innovation plays a part intermediary role in the impact of EC to promote the upgrading of industrial structure. Further analysis found that the larger the funds of compensation input, the higher the assessment intensity, the better the effects are. Finally, this study puts forward relevant policy recommendations to further release the potential of the horizontal EC for the reference of decision-makers.

The spillover effect of fiscal environmental protection spending on residents' medical and healthcare expenditure: evidence from China.

The Chinese government has not only been increasing investments in environmental protection, improving the quality of the ecological environment, but has also been focusing on curbing the excessive growth of medical and healthcare expenses so as to ease the economic burden of China's residents. Both aspects are significant concerns worldwide and have received much research attention individually, but the relationship between government environmental protection expenditure and residents' medical and healthcare expenditure remains unclear. Based on panel data from 31 provinces in China from 2007 to 2019, this paper empirically reveals that fiscal environmental protection expenditure is significantly negatively correlated with per capita medical and healthcare expenditure of residents (including outpatient expenditure and inpatient expenditure). This study shows that increasing the fiscal environmental protection expenditure can help curb the rising level of residents' medical and healthcare expenditure. In addition, the results of heterogeneity analysis indicate that the above relationship is stronger in provinces with a relatively low level of economic development or low proportion of the population over 65 years old. Management implications are discussed.

How can government environmental enforcement and corporate environmental responsibility consensus reduce environmental emergencies?

Environmental emergencies have become a severe challenge to human society due to their great harmfulness, which seriously restrict the sustainable development of human society. Previous studies mainly focused on the pollutant sources and consequences of environmental emergencies, yet lacking highlight of the antecedents of environmental emergencies. Based on a panel data of 31 provinces in China, this paper establishes a mediating effect model to empirically investigate how government environmental enforcement and corporate environmental responsibility consensus (CCER) influence environmental emergencies. The results show that there exists a significant positive correlation between the intensity of government environmental enforcement and CCER, while CCER has negative influence on the frequency of environmental emergencies. In addition, CCER plays a full mediating role in the relationship between government environmental enforcement and environmental emergencies. In short, this research clarified the relationship between government environmental enforcement, CCER, and environmental emergencies, verified the intermediary role of CCER. This paper uncovers another "black box" of the mechanism of environmental emergencies, makes up for the limitations of previous studies from a single perspective, expands the scope of research on environmental emergencies, and proposes corresponding policy recommendations.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.

miR-143 is implicated in growth plate injury by targeting IHH in precartilaginous stem cells.

Precartilaginous stem cells (PCSCs) are able to initiate chondrocyte and bone development. The present study aimed to investigate the role of miR-143 and the underlying mechanisms involved in PCSC proliferation. In a rat growth plate injury model, tissue from the injury site was collected and the expression of miR-143 and its potential targets was determined. PCSCs were isolated from the rabbits' distal epiphyseal growth plate. Cell viability, DNA synthesis, and apoptosis were determined with MTT, BrdU, and flow cytometric analysis, respectively. Real time PCR and western blot were performed to detect the mRNA and protein expression of the indicated genes. Indian hedgehog (IHH) was identified as a target gene for miR-143 with luciferase reporter assay. Decreased expression of miR-143 and increased expression of IHH gene were observed in the growth plate after injury. miR-143 mimics decreased cell viability and DNA synthesis and promoted apoptosis of PCSCs. Conversely, siRNA-mediated inhibition of miR-143 led to increased growth and suppressed apoptosis of PCSCs. Transfection of miR-143 decreased luciferase activity of wild-type IHH but had no effect when the 3'-UTR of IHH was mutated. Furthermore, the effect of miR-143 overexpression was neutralized by overexpression of IHH. Our study showed that miR-143 is involved in growth plate behavior and regulates PCSC growth by targeting IHH, suggesting that miR-143 may serve as a novel target for PCSC-related diseases.