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Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients' lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Administrative fragmentation among government agencies has posed a significant challenge to environmental governance. However, few studies have theoretically examined and empirically measured how local governments in China address this with collaborative approaches. Informed by the ecology of games framework (EGF), this paper examined the status of inter-agency water governance in Dongguan City: the pilot city of aquatic ecologically friendly city of China. The research question we seek to address is: What are the main characteristics of the water governance network in local China? With empirical data collected via survey questionnaires and semi-structured in-depth interviews following the EGF, we find that local agencies have realized the need for cooperation in water resource management. However, they were not substantially familiar with each other in the local policy network, failing to reach their full potential in collaboration. Social network analysis was used to analyze the collaborative network. We found that the network has a weak to moderate density, allowing for collective action problems and leading to insufficient cooperative governance. The network has presented central actors with strong bridging capital to control information and resource flows. We also find strong bonding capital among some policy actors as shown in high reciprocity, clustering coefficient and transitivity. To strengthen the effectiveness of local water governance, efforts should be made to establish a functional department and extend the boundary of collaborative network. [ABSTRACT FROM AUTHOR]

A series of new 2-phenyl-quinoline-4-carboxylic acid derivatives was synthesized starting from aniline, 2-nitrobenzaldehyde, pyruvic acid followed by Doebner reaction, amidation, reduction, acylation and amination. All of the newly-synthesized compounds were characterized by 1H-NMR, 13C-NMR and HRMS. The antibacterial activities of these compounds against Gram-negative (Escherichia coli, Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis), as well as one strain of methicillin-resistant Staphylococcus aureus (MRSA) bacteria were evaluated by the agar diffusion method (zone of inhibition) and a broth dilution method (minimum inhibitory concentration (MIC)), and their structure-activity relationships were obtained and discussed. The results revealed that some compounds displayed good antibacterial activity against Staphylococcus aureus, and Compounds 5a4 and 5a7 showed the best inhibition with an MIC value of 64 μg/mL against Staphylococcus aureus and with an MIC value of 128 μg/mL against Escherichia coli, respectively. The results of the MTT assay illustrated the low cytotoxicity of Compound 5a4. [ABSTRACT FROM AUTHOR]

O6-methylguanine-DNA methyltransferase (MGMT) activity is responsible for temozolomide (TMZ) resistance in patients harboring aggressive pituitary adenomas. Recently, disulfiram (DSF) has been shown to induce the loss of MGMT protein and increase TMZ efficacy in glioblastoma cells, while CD133+ nestin+ cells isolated from the cell population have been implicated as pituitary adenoma stem-like cells. However, whether DSF is able to potentiate the cytotoxic effects of TMZ on human pituitary adenoma cells has not been investigated to date. In the present study, CD133+ nestin+ phenotype cells were isolated from primary cultured human pituitary adenoma cells using microbeads. It was found that DSF reduced MGMT protein expression and sensitized human pituitary adenoma cells and stem-like cells to TMZ in vitro, while the proteasome inhibitor PS-341 abrogated the inhibitory effect of DSF on MGMT in vitro. The sensitizing effect of DSF was also verified in primary cultured human pituitary adenoma cells in vivo. The results of the present study suggested that DSF can increase the efficacy of the anti-tumor effect of TMZ on human pituitary adenoma cells and CD133+ nestin+ stem like cells via the ubiquitin-proteasomal MGMT protein elimination route. DSF combined with TMZ may be an effective therapeutic strategy against aggressive pituitary adenomas. [ABSTRACT FROM AUTHOR]

V-domain immunoglobulin suppressor of T-cell activation (VISTA) is a negative immune-checkpoint protein that suppresses T-cell responses. To determine whether VISTA synergizes with another immune-checkpoint, programmed death 1 (PD-1), this study characterizes the immune responses in VISTA-deficient, PD-1-deficient (KO) mice and VISTA/PD-1 double KO mice. Chronic inflammation and spontaneous activation of T cells were observed in both single KO mice, demonstrating their nonredundancy. However, the VISTA/PD-1 double KO mice exhibited significantly higher levels of these phenotypes than the single KO mice. When bred onto the 2D2 T-cell receptor transgenic mice, which are predisposed to development of inflammatory autoimmune disease in the CNS, the level of disease penetrance was significantly enhanced in the double KO mice compared with in the single KO mice. Consistently, the magnitude of T-cell response toward foreign antigens was synergistically higher in the VISTA/PD-1 double KO mice. A combinatorial blockade using monoclonal antibodies specific for VISTA and PD-L1 achieved optimal tumor-clearing therapeutic efficacy. In conclusion, our study demonstrates the nonredundant role of VISTA that is distinct from the PD-1/PD-L1 pathway in controlling T-cell activation. These findings provide the rationale to concurrently target VISTA and PD-1 pathways for treating T-cell-regulated diseases such as cancer. [ABSTRACT FROM AUTHOR]

Forest dynamics plots, which now span longitudes, latitudes, and habitat types across the globe, offer unparalleled insights into the ecological and evolutionary processes that determine how species are assembled into communities. Understanding phylogenetic relationships among species in a community has become an important component of assessing assembly processes. However, the application of evolutionary information to questions in community ecology has been limited in large part by the lack of accurate estimates of phylogenetic relationships among individual species found within communities, and is particularly limiting in comparisons between communities. Therefore, streamlining and maximizing the information content of these community phylogenies is a priority. To test the viability and advantage of a multi-community phylogeny, we constructed a multi-plot mega-phylogeny of 1347 species of trees across 15 forest dynamics plots in the Forest GEO network using DNA barcode sequence data (rbcL, matK, and psbA-trnH) and compared community phylogenies for each individual plot with respect to support for topology and branch lengths, which affect evolutionary inference of community processes. The levels of taxonomic differentiation across the phylogeny were examined by quantifying the frequency of resolved nodes throughout. In addition, three phylogenetic distance (PD) metrics that are commonly used to infer assembly processes were estimated for each plot [PD, Mean Phylogenetic Distance (MPD), and Mean Nearest Taxon Distance (MNTD)]. Lastly, we examine the partitioning of phylogenetic diversity among community plots through quantification of inter-community MPD and MNTD. Overall, evolutionary relationships were highly resolved across the DNA barcode-based mega-phylogeny, and phylogenetic resolution for each community plot was improved when estimated within the context of the mega-phylogeny. Likewise, when compared with phylogenies for individual plots, estimates of phylogenetic diversity in the mega-phylogeny were more consistent, thereby removing a potential source of bias at the plot-level, and demonstrating the value of assessing phylogenetic relationships simultaneously within a mega-phylogeny. An unexpected result of the comparisons among plots based on the mega-phylogeny was that the communities in the Forest GEO plots in general appear to be assemblages of more closely related species than expected by chance, and that differentiation among communities is very low, suggesting deep floristic connections among communities and new avenues for future analyses in community ecology. [ABSTRACT FROM AUTHOR]

The non-isothermal cure kinetics of a novel silicone modified waterborne curing agent and glycidyl ether of bisphenol A (DGEBA) based waterborne epoxy resin was studied by DSC measurement. The DSC results from different silicone contents and heating rates were calculated by the advanced isoconversional method (AICM). The results show that the effective activation energy of the curing reaction is changed with conversion and the curing process of waterborne epoxy resin systems are mainly controlled by diffusion. When silicone is incorporated, the effective activation energy is relatively lower at the beginning, then increased dramatically with the curing process, finally decreased slowly. The introduction of silicone into the waterborne epoxy system could shorten the entire curing time and facilitate the forming of cured films. [ABSTRACT FROM AUTHOR]