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Objective This meta-analysis aimed to evaluate the dose-response relationship between body mass index (BMI) and the risk of chemotherapy-induced peripheral neuropathy (CIPN) in cancer patients. Methods We conducted a dose-response meta-analysis of 10 studies involving 6,841 cancer patients. Studies reporting BMI and CIPN outcomes were selected. The relationship between BMI and CIPN was assessed using random-effects models and restricted cubic splines to model the dose-response association. Results Pooled analysis revealed a significant association between higher BMI and increased risk of CIPN, with an odds ratio (OR) of 1.55 (95% CI, 1.20–1.99). A dose-response analysis demonstrated a clear linear relationship between BMI and the risk of CIPN. For every 5 kg/m 2 increase in BMI, the relative risk of CIPN increased by approximately 15%. Subgroup analyses showed stronger associations in breast cancer patients and those treated with taxane or platinum-based regimens. Sensitivity analyses confirmed the robustness of the results, and mild publication bias was observed. Conclusions Higher BMI is significantly associated with an increased risk of CIPN, with a dose-dependent effect. Weight management interventions, such as dietary modifications and physical activity, may reduce CIPN risk, particularly in patients with elevated BMI undergoing chemotherapy with neurotoxic regimens.

With the increasing complexity of the shortwave communication environment, the efficiency and accuracy of the manual detection of Morse code no longer meet actual needs. Therefore, this paper proposes a Morse code detection algorithm called YFDM. For the time–frequency image of the received signal, a combination module of deformable convolution and C3 is used to enhance the backbone network’s attention to the abstract semantics and location information of Morse code. GSConv and VOV-GSCSP modules are used to build a lightweight neck network. Finally, the confidence propagation cluster (CP-Cluster) algorithm is used to filter the detection frame. In an ablation experiment, the parameters and giga floating-point operations per second (GFLOPs) of YFDM were 5.961 M and 9.74 G, respectively, 15.11% and 38.9% less than those of YOLOv5. Moreover, when WIoUv1 was used as the loss function of the bounding box, the AP0.5:0.95 and frames per second (FPS) values of the algorithm reached the highest values, 0.68 and 72.4. The experimental results indicate that the algorithm can effectively reduce the weight of the model while ensuring the detection accuracy and inference speed.

To mitigate climate change, advancing clean energy has become a critical policy objective. As a financial mechanism that supports environmental sustainability, green finance—particularly through pilot initiatives—plays a pivotal role in accelerating clean energy development. On the basis of DID from the panel data of 29 provincial places in China during the period 2010–2022, this study investigates the impact of 2017’s Green Finance Reform and Innovation Pilot Zones (GFRIPZ). The results have shown that the GFRIPZ policy significantly increased natural gas and electricity consumption while reducing coal’s share in the energy consumption structure. Further mediation analysis reveals that technological innovation acts as a principal transmission channel. Robustness is confirmed through PSM-DID and placebo tests. Heterogeneity analysis uncovers regional variation: while GFRIPZ policies boosted natural gas consumption across eastern, central, and western regions, the increase in electricity consumption was significant only in central and western provinces. These results highlight the differentiated regional effectiveness of green finance policies and underscore the need for region-specific implementation strategies.

Tissue microRNAs (miRNAs) can detect cancers and predict prognosis. Several recent studies reported that tissue, plasma, and saliva miRNAs share similar expression profiles. In this study, we investigated the discriminatory power of salivary miRNAs (including whole saliva and saliva supernatant) for detection of esophageal cancer. By Agilent microarray, six deregulated miRNAs from whole saliva samples from seven patients with esophageal cancer and three healthy controls were selected. The six selected miRNAs were subjected to validation of their expression levels by RT-qPCR using both whole saliva and saliva supernatant samples from an independent set of 39 patients with esophageal cancer and 19 healthy controls. Six miRNAs (miR-10b*, miR-144, miR-21, miR-451, miR-486-5p, and miR-634) were identified as targets by Agilent microarray. After validation by RT-qPCR, miR-10b*, miR-144, and miR-451 in whole saliva and miR-10b*, miR-144, miR-21, and miR-451 in saliva supernatant were significantly upregulated in patients, with sensitivities of 89.7, 92.3, 84.6, 79.5, 43.6, 89.7, and 51.3% and specificities of 57.9, 47.4, 57.9%, 57.9, 89.5, 47.4, and 84.2%, respectively. We found distinctive miRNAs for esophageal cancer in both whole saliva and saliva supernatant. These miRNAs possess discriminatory power for detection of esophageal cancer. Because saliva collection is noninvasive and convenient, salivary miRNAs show great promise as biomarkers for detection of esophageal cancer in areas at high risk.

Optically pure non-natural β -amino acids are noteworthy molecular motifs of numerous pharmaceutically important molecules. Skeletal editing of abundant α -amino acid scaffolds via tandem radical 1,2-N-shift/cross-coupling represents a powerful tool to straightforward assemble new β -amino acid molecules; however, this strategy presents substantial challenges owing to difficulties in reactivity and regio-/enantiocontrol. Herein, we report a cross-electrophile C(sp 2 )-C(sp 3 ) coupling of β -bromo α -amino acid esters with aryl bromides via a π -system-independent 1,2-N-shift, which allows access to α -arylated β -amino acid motifs with high efficiency and regioselectivity. Furthermore, upon the cooperative catalysis of the Ni(II)/ cyclo-Box complex and chiral phosphoric acid, this migratory coupling further achieves high enantioselectivity control in C(sp 3 )–C(sp 2 ) bond construction. In addition, detailed experimental studies and DFT calculations have been conducted to gain insight into the mechanism and origin of the enantioselectivity. Overall, this synergistic strategy expands these methods to the challenging enantioselective C(sp 2 )–C(sp 3 ) cross-electrophile coupling via π -system-independent radical 1,2-amino migration. The synthesis of β-amino acid molecules via tandem radical 1,2-N-shift/cross-coupling of α-amino acid scaffolds represents an attractive and powerful tool. Herein, the authors report the cross-electrophile C(sp2)-C(sp3) coupling of β-bromo α-amino acid esters with aryl bromides via a π-system-independent 1,2-N-shift, which allows access to α-arylated β-amino acid motifs with high efficiency and regioselectivity.

In network science, one of the significant and challenging subjects is the detection of communities. Modularity [1] is a measure of community structure that compares connectivity in the network with the expected connectivity in a graph sampled from a random null model. Its optimisation is a common approach to tackle the community detection problem. We present a new method for modularity maximisation, which is based on the observation that modularity can be expressed in terms of total variation on the graph and signless total variation on the null model. The resulting algorithm is of Merriman–Bence–Osher (MBO) type. Different from earlier methods of this type, the new method can easily accommodate different choices of the null model. Besides theoretical investigations of the method, we include in this paper numerical comparisons with other community detection methods, among which the MBO-type methods of Hu et al. [2] and Boyd et al. [3], and the Leiden algorithm [4].

Based on the panel data of 134 counties (cities and districts) from 1998 to 2017, the temporal-spatial variation characteristics and influencing factors of agricultural eco-efficiency in Shandong Province were analyzed by using various methods, such as the super-efficiency SBM (slacks-based measure) model considering undesired output and the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model, which helps clarify the improvements needed for agricultural eco-efficiency and provides a basis for the development of ecological agriculture in Shandong Province. Results showed the following: (1) During 1998-2017, the agricultural eco-efficiency of Shandong Province showed a fluctuating increasing tendency, but the overall efficiency value wasrelatively low. (2) The agricultural eco-efficiency of Shandong Province had a significant regional disparity, and its spatial agglomeration gradually weakened. The spatial distribution had a sporadic distribution of high value areas at first and then gradually formed the “low-high-low-high” zonal distribution from west to east. (3) The net income per capita of farmers and the added value of the primary industry had a significantly positive correlation with the agricultural eco-efficiency of Shandong Province, while the mechanization level, the planting area per capita, the level of financial support to agriculture and the planting structure exhibited a mainly negative correlation with the agricultural eco-efficiency of Shandong Province. Moreover, the added value of the primary industry and the financial support to agriculture in the 0.75 quantile had no significant influence on the agricultural eco-efficiency of Shandong Province, and the planting structure in the 0.25 and 0.75 quantiles also had no significant influence. RESUMO: Com base nos dados do painel de 134 municípios (cidades, distritos) na província de Shandong de 1998 a 2017, as características de variação espacial e temporal da ecoeficiência agrícola na província de Shandong foram analisadas usando vários métodos, como o modelo SBM (Medida baseada EM estacas) supereficiente. Considerando indesejados produção e modelo STIRPAT (Impactos estocásticos da regressão da população, da afluência e da tecnologia), ajudará a esclarecer a direção da melhoria da eco eficiência agrícola na província de Shandong e fornecerá uma base para o desenvolvimento da agricultura ecológica. Os resultados mostraram que (1) em 1998-2017, a ecoeficiência agrícola da província de Shandong mostrou uma tendência ascendente na flutuação, mas o valor geral da eficiência foi baixo. (2) A distribuição espacial da distribuição esporádica inicial da área de alto valor se formou gradualmente de oeste para leste, distribuição zonal “ “baixo-alto-baixo-alto”” (3) O lucro líquido per capita dos agricultores e o valor acrescentado da indústria primária foram significativamente correlacionados positivamente com a ecoeficiência agrícola da província de Shandong. O nível de mecanização, a área de plantio per capita, o apoio financeiro ao nível agrícola e a estrutura de plantio, entre eles, o valor acrescentado da indústria primária e o apoio financeiro à agricultura em 0,75 quantil, a estrutura de plantio em 0,25 e 0,75 quantil na ecoeficiência agrícola da província de Shandong não é significativa.

Water management is a crucial aspect in the efficient functioning of proton exchange membrane fuel cells (PEMFCs). The presence of a two-phase flow, consisting of water and reactive gas, in the channel of the PEMFC is of utmost importance for effective water management. This study focuses on investigating the removal of liquid water in 3D wave channels and 2D straight channels using the volume of fluid method. The study analyzes the dynamic behavior of droplets emerging from the gas diffusion layer (GDL) into the channel under the influence of gas flow. The study also explores the effects of droplet growth, deformation, detachment, force, and pore size on critical water behavior and water content in the channel. The results indicate that the 3D wave channel is more effective in removing liquid water compared to the 2D straight channel. It is observed that increasing the velocity facilitates the discharge of liquid water. However, excessively high velocities lead to parasitic power losses. Furthermore, while larger pore sizes in the GDL are not advantageous for PEMFC performance, a moderate increase in pore size aids in the discharge of liquid water. The knowledge gained through this study deepens the understanding of droplet dynamics in PEMFC gas channels and assists in optimizing the design and operational conditions of these channels.

Cooking emissions can potentially contribute to secondary organic aerosol (SOA) but remain poorly understood. In this study, formation of SOA from gas-phase emissions of five heated vegetable oils (i.e., corn, canola, sunflower, peanut and olive oils) was investigated in a potential aerosol mass (PAM) chamber. Experiments were conducted at 19–20 °C and 65–70 % relative humidity (RH). The characterization instruments included a scanning mobility particle sizer (SMPS) and a high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS). The efficiency of SOA production, in ascending order, was peanut oil, olive oil, canola oil, corn oil and sunflower oil. The major SOA precursors from heated cooking oils were related to the content of monounsaturated fat and omega-6 fatty acids in cooking oils. The average production rate of SOA, after aging at an OH exposure of 1. 7 × 1011 molecules cm−3 s, was 1. 35 ± 0. 30 µg min−1, 3 orders of magnitude lower compared with emission rates of fine particulate matter (PM2. 5) from heated cooking oils in previous studies. The mass spectra of cooking SOA highly resemble field-derived COA (cooking-related organic aerosol) in ambient air, with R2 ranging from 0.74 to 0.88. The average carbon oxidation state (OSc) of SOA was −1.51 to −0.81, falling in the range between ambient hydrocarbon-like organic aerosol (HOA) and semi-volatile oxygenated organic aerosol (SV-OOA), indicating that SOA in these experiments was lightly oxidized.

Time series analysis is of critical importance in a wide range of applications, including weather forecasting, anomaly detection, and action recognition. Accurate time series forecasting requires modeling complex temporal dependencies, particularly multi-scale periodic patterns. To address this challenge, we propose a novel Wavelet-Enhanced Transformer (Wave-Net). Wave-Net transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. This paper introduces Wave-Net, a model that incorporates wavelet and Fourier transforms for feature extraction, along with an enhanced cycle offset and optimized dynamic K for improved robustness. The Transformer layer is further refined to bolster long-term modeling capabilities. Evaluations on real-world benchmarks demonstrate that Wave-Net consistently achieves state-of-the-art performance across mainstream time series analysis tasks.