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Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.

Zihang Feng,1,* Wenxia Lu,2,* Yu Xie3 1Southern Medical University, Guangzhou, Guangdong Province, People's Republic of China; 2Nanjing Medical University, Nanjing, Jiangsu Province, People's Republic of China; 3Obstetrics and Gynecology Hospital of Fudan University, Shanghai, People's Republic of China*These authors contributed equally to this workCorrespondence: Yu Xie, Obstetrics and Gynecology Hospital of Fudan University, 419 Fangxie Road, Shanghai 200011, People's Republic of China, Tel +86-021-33189900, Fax +86-021-63450900, Email yuxie18@fudan.edu.cn View the original paper by Mr Ashrafizadeh and colleagues

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue. This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students. A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time. A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups. This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach. Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.

In “Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial” (J Med Internet Res 2025;27: e71741.) the authors made fourteen clarifications.

Objective Most both‐column acetabular fractures are combined with posterior wall fragments. However, the morphology of this posterior wall is varied, and how to fix this posterior wall remains a controversial topic. To investigate the morphological characteristics of posterior wall fragments of both‐column acetabular fractures and select corresponding fixation methods. Methods Data from 352 patients with acetabular fractures admitted to the level one trauma centre in our hospital between January 2006 and December 2022 were collected. The morphology of posterior wall fragments was observed and analyzed in 83 cases of both‐column acetabular fractures and classified according to the consistency of posterior wall morphology. A fracture map of the posterior wall was created on a normal template according to the three morphological types of posterior wall fragments. Finally, the high‐incidence area of the posterior wall fracture was projected onto the iliac fossa and the medial side of the posterior column to guide the fixation of the posterior wall fragment using the anterior intrapelvic approach. Results Fractures were divided into four types: I, large posterior wall fragment which was high in the ilium bone (34 cases, 41.0%); II, posterior wall fragment in the acetabular parietal region (18 cases, 21.7%); III, posterior wall marginal fracture (10 cases, 12.0%); and IV, non‐combined posterior wall fracture (21 cases, 25.3%). The most common morphologies of the posterior wall fragments of the first two types were mapped and projected onto the anterior iliac inner plate and medial side of the posterior column, where the corresponding area could be used to guide the insertion of the internal fixation. Conclusion Both‐column acetabular fractures combined with posterior wall fractures can be divided into four types according to the morphology of the posterior wall fragment. Understanding the corresponding three‐dimensional morphology and projection position of different types of these fragments can help surgeons determine the position and orientation of internal fixation of posterior wall fractures. By observing the posterior wall fragments of acetabular both‐column fractures, we divided them into four types according to the morphology of the posterior wall fragment. The two most common types of posterior wall fractures are projected onto the medial side of the pelvis to facilitate anterior fixation of the posterior wall fragments.

Dysmorphic pulmonary vascular growth and abnormal endothelial cell (EC) proliferation are paradoxically observed in premature infants with bronchopulmonary dysplasia (BPD), despite vascular pruning. The pentose phosphate pathway (PPP), a metabolic pathway parallel to glycolysis, generates NADPH as a reducing equivalent and ribose 5-phosphate for nucleotide synthesis. It is unknown whether hyperoxia, a known mediator of BPD in rodent models, alters glycolysis and the PPP in lung ECs. We hypothesized that hyperoxia increases glycolysis and the PPP, resulting in abnormal EC proliferation and dysmorphic angiogenesis in neonatal mice. To test this hypothesis, lung ECs and newborn mice were exposed to hyperoxia and allowed to recover in air. Hyperoxia increased glycolysis and the PPP. Increased PPP, but not glycolysis, caused hyperoxia-induced abnormal EC proliferation. Blocking the PPP reduced hyperoxia-induced glucose-derived deoxynucleotide synthesis in cultured ECs. In neonatal mice, hyperoxia-induced abnormal EC proliferation, dysmorphic angiogenesis, and alveolar simplification were augmented by nanoparticle-mediated endothelial overexpression of phosphogluconate dehydrogenase, the second enzyme in the PPP. These effects were attenuated by inhibitors of the PPP. Neonatal hyperoxia augments the PPP, causing abnormal lung EC proliferation, dysmorphic vascular development, and alveolar simplification. These observations provide mechanisms and potential metabolic targets to prevent BPD-associated vascular dysgenesis.

The pathological advancement of osteoporosis is caused by the uneven development of bone marrow-derived mesenchymal stem cells (BMSCs) in terms of osteogenesis and adipogenesis. While the role of EEF1B2 in intellectual disability and tumorigenesis is well established, its function in the bone-fat switch of BMSCs is still largely unexplored. During the process of osteogenic differentiation, we observed an increase in the expression of EEF1B2, while a decrease in its expression was noted during adipogenesis. Suppression of EEF1B2 hindered the process of osteogenic differentiation and mineralization while promoting adipogenic differentiation. On the contrary, overexpression of EEF1B2 enhanced osteogenesis and strongly inhibited adipogenesis. Furthermore, the excessive expression of EEF1B2 in the tibias has the potential to mitigate bone loss and decrease marrow adiposity in mice with osteoporosis. In terms of mechanism, the suppression of β-catenin activity occurred when EEF1B2 function was suppressed during osteogenesis. Our collective findings indicate that EEF1B2 functions as a regulator, influencing the differentiation of BMSCs and maintaining a balance between bone and fat. Our finding highlights its potential as a therapeutic target for diseases related to bone metabolism.

Ehlers-Danlos syndrome, Classic-Like, 2 (clEDS2) is a rare genetic disorder caused by biallelic mutations in the AEBP1 gene, which encodes aortic carboxypeptidase-like protein (ACLP). Patients with clEDS2 exhibit hallmark features such as loose connective tissues, osteoporosis, and scoliosis. Despite its clinical significance, the molecular mechanisms underlying AEBP1 mutations in skeletal development remain poorly understood, and effective therapeutic strategies are currently unavailable. Here, using OsxCre conditional KO mice, we show that Aebp1 deletion in osteoprogenitors reduces body size and bone mass, recapitulating key skeletal features reported in clEDS2. In primary osteoblasts, both genetic deletion and siRNA-mediated knockdown of Aebp1 impair osteoblast differentiation. Mechanistically, Aebp1 loss attenuates Wnt/β-catenin signaling in bone. Restoration of Wnt/β-catenin signaling by injecting BIO, a small molecule inhibitor of GSK3, substantially rescued bone mass reduction in Aebp1-KO mice. These findings support a model in which Aebp1 sustains baseline Wnt/β-catenin tone in osteoblast-lineage cells and suggest that Wnt-targeted approaches may help mitigate clEDS2-related skeletal defects.