SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases.

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.

Identification of leaf diseases in field crops based on improved ShuffleNetV2.

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Exploring the synergies between digital finance and clean energy: a case study of green bond spillover effects.

The impact of digital finance on green bonds and clean energy sources is analyzed. So, the primary objective of this study is to create a unique time-varying causality test to identify the relationship between ecological consciousness and green technology, clean energy, and digital currency. The China region was established using data from 2001 to 2019. A dynamic connection model with spillover is employed to further guarantee stability. The empirical findings reveal that the clean energy to digital finance index (30.544%) and the clean energy to green economy index (30.544%) are the sources for the spillover shocks analysis. because the overall dynamic connection of assets throughout time is affected by financial events. For every additional percentage point that renewable energy sources are employed, long-term environmental costs are lowered by 0.68%. And yet, the transition from renewable energy to digital finance has been characterised by increasing instability and causal significance as it has advanced. The organizational environment is shown to have the second-greatest influence on the growth of the green bond market, behind the state of the local economy and environmental governance. Increasing the marketability of clean energy securities requires stable, predictable legislation that improves our knowledge of the risk profile of these investments.

Hormonal Balance, Photosynthesis, and Redox Reactions in the Leaves of Caragana korshinskii Kom. under Water Deficit.

To evaluate the physiological responses of Korshinsk peashrub (Caragana korshinskii Kom.) to water deficit, photosynthetic gas exchange, chlorophyll fluorescence, and the levels of superoxide anion (O2•-), hydrogen peroxide (H2O2), malondialdehyde (MDA), antioxidant enzymes, and endogenous hormones in its leaves were investigated under different irrigation strategies during the entire growth period. The results showed that leaf growth-promoting hormones were maintained at a higher level during the stages of leaf expansion and vigorous growth, and zeatin riboside (ZR) and gibberellic acid (GA) gradually decreased with an increase in water deficit. At the leaf-shedding stage, the concentration of abscisic acid (ABA) dramatically increased, and the ratio of ABA to growth-promoting hormones increased to a high level, which indicated that the rate of leaf senescence and shedding was accelerated. At the stages of leaf expansion and vigorous growth, the actual efficiency of photosystem II (PSII) (ΦPSii) was downregulated with an increment in non-photochemical quenching (NPQ) under moderate water deficit. Excess excitation energy was dissipated, and the maximal efficiency of PSII (Fv/Fm) was maintained. However, with progressive water stress, the photo-protective mechanism was inadequate to avoid photo-damage; Fv/Fm was decreased and photosynthesis was subject to non-stomatal inhibition under severe water deficit. At the leaf-shedding stage, non-stomatal factors became the major factors in limiting photosynthesis under moderate and severe water deficits. In addition, the generation of O2•- and H2O2 in the leaves of Caragana was accelerated under moderate and severe water deficits, which caused an enhancement of antioxidant enzyme activities to maintain the oxidation-reduction balance. However, when the protective enzymes were insufficient in eliminating excessive reactive oxygen species (ROS), the activity of catalase (CAT) was reduced at the leaf-shedding stage. Taken all together, Caragana has strong drought resistance at the leaf expansion and vigorous growth stages, but weak drought resistance at the leaf-shedding stage.

Effectiveness and degradation pathways of bisphenol A (BPA) initiated by hydroxyl radicals and sulfate radicals in water: Initial reaction sites based on DFT prediction.

Bisphenol A (BPA), one of the widely known endocrine-disrupting chemicals, can be effectively degraded by advanced oxidation processes in water because of the powerful reactive oxygen species. In this study, Fenton, UV/Fenton, and metal ion/peroxymonosulfate (PMS) processes were compared to investigate BPA degradation efficiency and pathways initiated by hydroxyl radicals and sulfate radicals. In contrast to the Fenton system, which only degraded 60% of BPA within 15 min, the UV/Fenton system could degrade greater than 80% of BPA, because more hydroxyl radicals (•OH) were generated under the reduction of Fe3+ to Fe2+. The optimized parameters of the UV/Fenton system were as follows: 8 µmol/L of Fe2+, 80 µmol/L of H2O2, and a pH value of 3.0. As for the metal ion/PMS system, the BPA degradation efficiency was closely associated with the applied metal ions, and the order was as follows: Co2+/PMS (∼100%) > Fe2+/PMS (∼80%) > Cu2+/PMS (∼79%). The degradation pathways of BPA were theoretically interpreted through density functional theory prediction and degradation products during various processes. Two major initial reaction sites (4C and 6C) for •OH initiated using the UV/Fenton system and one initial reaction site (4C) for sulfate radicals (SO4•-) using the metal ion/PMS system were recognized for BPA degradation processes. The degradation products by •OH showed a larger average molecular weight than those by SO4•-. These studies are instructive for the application of different advanced oxidation systems in the treatment process of BPA in wastewater.

CARDIOPULMONARY BYPASS-DERIVED PLASMA EXOSOMAL HMGB1 CONTRIBUTES TO ALVEOLAR EPITHELIAL CELL NECROPTOSIS VIA mtDNA/CGAS/STING PATHWAY.

ABSTRACT: Our previous study confirmed that cardiopulmonary bypass (CPB) leads to acute lung injury (ALI) via inducing high-mobility group box 1 (HMGB1) release. Recent research showed that HMGB1 promotes pulmonary injury mainly via exosomes transport. Currently, alveolar epithelial cell (AEC) necroptosis has been demonstrated to be involved in ALI. However, it is unknown whether exosomal inflammatory cytokine HMGB1 promotes ALI by inducing AEC necroptosis, and its underlying mechanisms remain elusive. Here, a prospective cohort study was carried out, in which plasma samples from 21 CPB patients were isolated at four specific time points: pre-CPB, 2, 12, and 24 h after initiation of CPB. Plasma exosomes were extracted via ultra-high-speed centrifugation and cocultured with AEC cell line-A549 cells at increasing concentrations of 50, 100, and 150 µg/mL. Then, HMGB1 antagonist-Box A and mtDNA deficiency ethidium bromide (EtBr) were applied to explore the underlying role of exosomal HMGB1 and cytoplasm mitochondrial DNA in AEC. Western blot analysis showed that plasma exosomal HMGB1 expression gradually increased and peaked at 24 h after CPB. Twenty-four-hour treatment of CPB-derived exosomes at 150 µg/mL for 24 h could induce necroptosis by promoting mitochondrial fission and further elevating cytoplasm mtDNA levels in A549 cells, which was successfully blocked by Box A or EtBr. Most importantly, EtBr significantly inhibited cytoplasm mtDNA downstream guanosine monophosphate (GMP)-AMP synthase (cGAS)/stimulator of interferon gene (STING) signal pathway. Collectively, these data demonstrate that CPB-derived plasma exosomal HMGB1 contributes to AEC necroptosis through the mtDNA/cGAS/STING pathway.