Omnidirectional image super-resolution via position attention network.

For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However, ERP ODIs suffer from serious geometric distortion and pixel stretching across latitudes, generating massive redundant information at high latitudes. This characteristic poses a huge challenge for the traditional SR methods, which can only obtain the suboptimal ODISR performance. To address this issue, we propose a novel position attention network (PAN) for ODISR in this paper. Specifically, a two-branch structure is introduced, in which the basic enhancement branch (BE) serves to achieve coarse deep feature enhancement for extracted shallow features. Meanwhile, the position attention enhancement branch (PAE) builds a positional attention mechanism to dynamically adjust the contribution of features at different latitudes in the ERP representation according to their positions and stretching degrees, which achieves the enhancement for the differentiated information, suppresses the redundant information, and modulate the deep features with spatial distortion. Subsequently, the features of two branches are fused effectively to achieve the further refinement and adapt the distortion characteristic of ODIs. After that, we exploit a long-term memory module (LM), promoting information interactions and fusions between the branches to enhance the perception of the distortion, aggregating the prior hierarchical features to keep the long-term memory and boosting the ODISR performance. Extensive results demonstrate the state-of-the-art performance and the high efficiency of our PAN in ODISR.

Therapeutic Potential of Essential Oils Against Ulcerative Colitis: A Review.

Ulcerative colitis (UC) is a chronic non-sp ecific inflammatory disease of the colorectal mucosa. Researchers have associated UC onset with familial genetics, lifestyle behavior, inflammatory immune factors, intestinal microbiota, and the integrity of the intestinal mucosal barrier. The primary therapeutic interventions for UC consist of pharmacological management to control inflammation and promote mucosal healing and surgical interventions. The available drugs effectively control and decelerate the progression of UC in most patients; nonetheless, their long-term administration can exert adverse effects and influence the therapeutic effect. Plant essential oils (EOs) refer to a group of hydrophobic aromatic volatile substances. EOs have garnered considerable attention in both domestic and international research because of their anti-inflammatory, antibacterial, and antioxidant properties. They include peppermint, peppercorns, rosemary, and lavender, among others. Researchers have investigated the role of EOs in medicine and have elucidated their potential to mitigate the detrimental effects of UC through their anti-inflammatory, antioxidant, antidepressant, and anti-insomnia properties as well as their ability to regulate the intestinal flora. Furthermore, EOs exert minimal toxic adverse effects, further enhancing their appeal for therapeutic applications. However, these speculations are based on theoretical experiments, thereby warranting more clinical studies to confirm their effectiveness and safety. In this article, we aim to provide an overview of the advancements in utilizing natural medicine EOs for UC prevention and treatment. We will explore the potential pathogenesis of UC and examine the role of EOs therapy in basic research, quality stability, and management specification of inadequate EOs for UC treatment. We intend to offer novel insights into the use of EOs in UC prevention and management.

DeforT: Deformable transformer for visual tracking.

Most trackers formulate visual tracking as common classification and regression (i.e., bounding box regression) tasks. Correlation features that are computed through depth-wise convolution or channel-wise multiplication operations are input into both the classification and regression branches for inference. However, this matching computation with the linear correlation method tends to lose semantic features and obtain only a local optimum. Moreover, these trackers use an unreliable ranking based on the classification score and the intersection over union (IoU) loss for the regression training, thus degrading the tracking performance. In this paper, we introduce a deformable transformer model, which effectively computes the correlation features of the training and search sets. A new loss called the quality-aware focal loss (QAFL) is used to train the classification network; it efficiently alleviates the inconsistency between the classification and localization quality predictions. We use a new regression loss called α-GIoU to train the regression network, and it effectively improves localization accuracy. To further improve the tracker's robustness, the candidate object location is predicted by using a combination of online learning scores with a transformer-assisted framework and classification scores. An extensive experiment on six testing datasets demonstrates the effectiveness of our method. In particular, the proposed method attains a success score of 71.7% on the OTB-2015 dataset and an AUC score of 67.3% on the NFS30 dataset, respectively.

Deciphering the antidepressant effects of Rosa damascena essential oil mediated through the serotonergic synapse signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Rosa damascena is an ancient plant with significance in both medicine and perfumery that have a variety of therapeutic properties, including antidepressant, anti-anxiety, and anti-stress effects. Rose damascena essential oil (REO) has been used to treat depression, anxiety and other neurological related disorders in Iranian traditional medicine. However, its precise mechanism of action remains elusive. AIM OF THE STUDY: The aim of this study was to investigate the impact and mechanism underlying the influence of REO on chronic unpredictable mild stress (CUMS) rats. MATERIALS AND METHODS: Gas chromatography-mass spectrometry (GC-MS) technique coupling was used to analyze of the components of REO. A CUMS rat model was replicated to assess the antidepressant effects of varying doses of REO. This assessment encompassed behavioral evaluations, biochemical index measurements, and hematoxylin-eosin staining. For a comprehensive analysis of hippocampal tissues, we employed transcriptomics and incorporated weighting coefficients by means of network pharmacology. These measures allowed us to explore differentially expressed genes and biofunctional pathways affected by REO in the context of depression treatment. Furthermore, GC-MS metabolomics was employed to assess metabolic profiles, while a joint analysis in Metscape facilitated the construction of a network elucidating the links between differentially expressed genes and metabolites, thereby elucidating potential relationships and clarifying key pathways regulated by REO. Finally, the expression of relevant proteins in the key pathways was determined through immunohistochemistry and Western blot analysis. Molecular docking was utilized to investigate the interactions between active components and key targets, thereby validating the experimental results. RESULTS: REO alleviated depressive-like behavior, significantly elevated levels of the neurotransmitter 5-hydroxytryptamine (5-HT), and reduced hippocampal neuronal damage in CUMS rats. This therapeutic effect may be associated with the modulation of the serotonergic synapse signaling pathway. Furthermore, REO rectified metabolic disturbances, primarily through the regulation of amino acid metabolic pathways. Joint analysis revealed five differentially expressed genes (EEF1A1, LOC729197, ATP8A2, NDST4, and GAD2), suggesting their potential in alleviating depressive symptoms by modulating the serotonergic synapse signaling pathway and tryptophan metabolism. REO also modulated the 5-HT2A-mediated extracellular regulated protein kinases-cAMP-response element binding protein-brain-derived neurotrophic factor (ERK-CREB-BDNF) pathway. In addition, molecular docking results indicated that citronellol, geraniol and (E,E)-farnesol in REO may serve as key active ingredients responsible for its antidepressant effects. CONCLUSIONS: This study is the first to report that REO can effectively alleviate CUMS-induced depression-like effects in rats. Additionally, the study offers a comprehensive understanding of its intricate antidepressant mechanism from a multi-omics and multi-level perspective. Our findings hold promise for the clinical application and further development of this essential oil.

AMHGCN: Adaptive multi-level hypergraph convolution network for human motion prediction.

Human motion prediction is the key technology for many real-life applications, e.g., self-driving and human-robot interaction. The recent approaches adopt the unrestricted full-connection graph representation to capture the relationships inside the human skeleton. However, there are two issues to be solved: (i) these unrestricted full-connection graph representation methods neglect the inherent dependencies across the joints of the human body; (ii) these methods represent human motions using the features extracted from a single level and thus can neither fully exploit the various connection relationships among the human body nor guarantee the human motion prediction results to be reasonable. To tackle the above issues, we propose an adaptive multi-level hypergraph convolution network (AMHGCN), which uses the adaptive multi-level hypergraph representation to capture various dependencies among the human body. Our method has four different levels of hypergraph representations, including (i) the joint-level hypergraph representation to capture inherent kinetic dependencies in the human body, (ii) the part-level hypergraph representation to exploit the kinetic characteristics at a higher level (in comparison to the joint-level) by viewing some part of the human body as an entirety, (iii) the component-level hypergraph representation to model the semantic information, and (iv) the global-level hypergraph representation to extract long-distance dependencies in the human body. In addition, to take full advantage of the knowledge carried in the training data, we propose a reverse loss (i.e., adopting the future human poses to predict the historical poses reversely) to realize data augmentation. Extensive experiments show that our proposed AMHGCN can achieve state-of-the-art performance on three benchmarks, i.e., Human3.6M, CMU-Mocap, and 3DPW.

Polymorphs of Nb2O5 Compound and Their Electrical Energy Storage Applications.

Niobium pentoxide (Nb2O5), as an important dielectric and semiconductor material, has numerous crystal polymorphs, higher chemical stability than water and oxygen, and a higher melt point than most metal oxides. Nb2O5 materials have been extensively studied in electrochemistry, lithium batteries, catalysts, ionic liquid gating, and microelectronics. Nb2O5 polymorphs provide a model system for studying structure-property relationships. For example, the T-Nb2O5 polymorph has two-dimensional layers with very low steric hindrance, allowing for rapid Li-ion migration. With the ever-increasing energy crisis, the excellent electrical properties of Nb2O5 polymorphs have made them a research hotspot for potential applications in lithium-ion batteries (LIBs) and supercapacitors (SCs). The basic properties, crystal structures, synthesis methods, and applications of Nb2O5 polymorphs are reviewed in this article. Future research directions related to this material are also briefly discussed.

Recent Advances in NIR or X-ray Excited Persistent Luminescent Materials for Deep Bioimaging.

Due to their persistent luminescence, persistent luminescent (PersL) materials have attracted great interest. In the biomedical field, the use of persistent luminescent nanoparticles (PLNPs) eliminates the need for continuous in situ excitation, thereby avoiding interference from tissue autofluorescence and significantly improving the signal-to-noise ratio (SNR). Although persistent luminescence materials can emit light continuously, the luminescence intensity of small-sized nanoparticles in vivo decays quickly. Early persistent luminescent nanoparticles were mostly excited by ultraviolet (UV) or visible light and were administered for imaging purposes through ex vivo charging followed by injection into the body. Limited by the low in vivo penetration depth, UV light cannot secondary charge PLNPs that have decayed in vivo, and visible light does not penetrate deep enough to reach deep tissues, which greatly limits the imaging time of persistent luminescent materials. In order to address this issue, the development of PLNPs that can be activated by light sources with superior tissue penetration capabilities is essential. Near-infrared (NIR) light and X-rays are widely recognized as ideal excitation sources, making persistent luminescent materials stimulated by these two sources a prominent area of research in recent years. This review describes NIR and X-ray excitable persistent luminescence materials and their recent advances in bioimaging.

The JAK/STAT/NF-κB signaling pathway can be regulated by rosemary essential oil, thereby providing a potential treatment for DNCB-induced in mice.

OBJECTIVE: The purpose of this study was to investigate the mechanism through which rosemary essential oil treats atopic dermatitis. METHODS: A dinitrochlorobenzene (DNCB)-induced atopic dermatitis mouse model was established and treated with low (1%), medium (2%), and high (4%) doses of Rosmarinus officinalis essential oil (EORO). Serum levels of interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α) in each group were determined using enzyme-linked immunosorbent assay (ELISA). Skin tissues were stained with hematoxylin-eosin and toluidine blue. We used network pharmacology and molecular docking techniques to verify the biological activity of essential proteins and their corresponding compounds in the pathway. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics analysis and multivariate statistical analysis of mouse serum to screen differential metabolites and metabolic pathway analysis. Protein expression of p-JAK1, CD4+ cells, and IL-4 in the skin tissue was detected by immunohistochemistry analysis. Protein levels of STAT3, p-STAT3, P65, and p-P65 in damaged skin tissues were detected using western blotting. RESULT: The skin of mice in the model group showed different degrees of erythema, dryness, scratches, epidermal erosion and shedding, and crusting. After treatment, the serum levels of IL-6 and TNF-α in EORO group were significantly decreased, and the expression of p-JAK1,CD4 + cells, IL-4, p-P65 / P65 and p-STAT3 / STAT3 proteins in skin tissues were decreased. CONCLUSION: EORO can effectively improve DNCB-induced AD-like skin lesions in mice by regulating the JAK/STAT/NF-κB signaling pathway, thereby reducing the production of downstream arachidonic acid metabolites, inhibiting skin inflammation, and restoring epidermal barrier function.

Incentive or constraint? Comprehensive impacts of green credit policy on industrial energy intensity.

Green credit policy (GCP) has dual attributes of being both an "environmental regulation" and a "financial instrument". Understanding its role in facilitating industrial green transformation is crucial. However, there is limited theoretical and empirical evidence on the impact of GCP on industrial green transformation. This research fills this gap by comprehensively investigating the impacts and mechanisms of GCP on industrial energy intensity (EI) in China, considering both incentive and constraint effects. Theoretically, the environmental and financial impacts of GCP are merged into a unified analytical framework based on a heterogeneous enterprise model. Empirically, diverse empirical methods, including difference-in-differences (DID), difference-in-differences-in-differences (DDD), and mediating effects models, are adopted to examine whether GCP can promote green innovation or accelerate financial constraints. Results show that (1) GCP significantly decreases EI, especially among high-polluting enterprises (HPEs). The impact of incentives is far greater than that of constraints. (2) Regarding the incentive effect, energy substitution and innovation offsets exert a primary influence on reducing EI. (3) The constraint effect is caused primarily by rising financing and pollution abatement costs. (4) Heterogeneity analysis shows that the inhibiting effect of GCP is more significant in non-state-owned enterprises, underdeveloped financial markets, and abundant energy endowments. This paper provides a theoretical framework and new empirical evidence for policymakers to design effective policies for promoting industrial green transformation.

FAPbBr3@PbBr(OH) phosphor with high stability for anti-counterfeiting application via water induction.

Organic-inorganic hybrid perovskite nanocrystals have become a very widely used as semiconductor light-emitting materials. However, perovskite nanocrystals face stability challenges, which is a key factor hindering their application. In this paper, by introducing water into the synthesis of formamidinium lead bromide (FAPbBr3) perovskite, ultra-stable FAPbBr3@PbBr(OH) fluorescent material was prepared. The photoluminescence intensity of the material after the addition of water increased 2.9 times compared with that before the addition of water. The excellent green fluorescence emission was still maintained after four cycles of wash-dry treatment. Meanwhile, it also exhibits good ultraviolet and thermal stability. The above enhanced performance of FAPbBr3nanocrystals is attributed the protection of PbBr(OH). In addition, the prepared material can be used in anti-counterfeit patterns. The anti-counterfeit patterns have good color rendering and the luminous color has a high dependence on temperature. Both of these features make it very valuable for various fluorescent anti-counterfeiting labels.

Green industrial transition: Leveraging environmental innovation and environmental tax to achieve carbon neutrality. Expanding on STRIPAT model.

Anthropogenic global warming strategies on carbon mitigation are driven by encouraging green innovation and using carbon taxes, yet an empirical model to validate this is non-existing. Moreover, the existing stochastic effects by regression on population, wealth, and technology (STIRPAT) model has been found to lack policy tools on taxes and institutions that cut carbon emissions. This study amends the STIRPAT model with environmental technology, environmental taxes, and strong institutional frameworks to create a new model STIRPART(stochastic impacts by regression on population, affluence, regulation, and technology) to understand the factors impacting carbon pollution using the emerging 7 economies. Using data from 2000 to 2020, the Driscoll-Kraay fixed effects are employed in this analysis to conduct evidential tests of the impacts of environmental policies, eco-friendly innovations, and strong institutions. The outcomes indicate that environmental technology, environmental taxation, and institution quality decrease E7's carbon emissions by 0.170%, 0.080%, and 0.016%, respectively. It is recommended that E7 policymakers should adopt the STIRPART postulate as the theoretical basis for policies favoring environmental sustainability. The key contribution is the amendment of the STIRPAT model and the enhancement of the market-based mechanisms, such as patents, strong institutions, and carbon taxes, to enable environmental policy to be carried out sustainably and cost-effectively.

Robust fall detection in video surveillance based on weakly supervised learning.

Fall event detection has been a research hotspot in recent years in the fields of medicine and health. Currently, vision-based fall detection methods have been considered the most promising methods due to their advantages of a non-contact characteristic and easy deployment. However, the existing vision-based fall detection methods mainly use supervised learning in model training and require much time and energy for data annotations. To address these limitations, this work proposes a detection method that uses a weakly supervised learning-based dual-modal network. The proposed method adopts a deep multiple instance learning framework to learn the fall events using weak labels. As a result, the proposed method does not require time-consuming fine-grained annotations. The final detection result of each video is obtained by integrating the information obtained from two streams of the dual-modal network using the proposed dual-modal fusion strategy. Experimental results on two public benchmark datasets and a proposed dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods.

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications.

CO2 emissions are unevenly distributed both globally and regionally within nation-states. Given China's entrance into the new stage of economic development, an updated study on the largest CO2 emitter's domestic emission distribution is needed for effective and coordinated global CO2 mitigation planning. We discovered that domestic CO2 emissions in China are increasingly polarized for the 2007-2017 period. Specifically, the domestically exported CO2 emissions from the less developed and more polluting northwest region to the rest of China has drastically increased from 165 Mt in 2007 to 230 Mt in 2017. We attribute the polarizing trend to the simultaneous industrial upgrading of all regions and the persistent disparity in the development and emission decoupling of China's regions. We also noted that CO2 emissions exported from China to the rest of the world has decreased by 41% from 2007 to 2017, with other developing countries filling up the vacancy. As this trend is set to intensify, we intend to send an alarm message to policy makers to devise and initiate actions and avoid the continuation of pollution migration.

Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: A multi-dataset study.

PURPOSE: Classifying the subtypes of non-small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post-operative examination at present. Based on multi-center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). METHODS: We collected a multi-center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n = 97) and lung3 set (n = 71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ2,1 -norm minimization was used for feature selection and the importance of selected features was analyzed. Next, the prediction and generalization performance of 130 radiomics models (10 common algorithms and 120 heterogeneous ensemble combinations) were compared by the average AUC value on three test sets. Finally, predictive results of the optimal model were shown. RESULTS: After feature selection, 401 features were obtained. Features of intensity, texture GLCM, GLRLM, and GLSZM had higher classification weight coefficients than other features (shape, texture GLDM, and NGTDM), and the filtered image features exhibited significant importance than original image features (p-value = 0.0210). Moreover, five ensemble learning algorithms (Bagging, AdaBoost, RF, XGBoost, GBDT) had better generalization performance (p-value = 0.00418) than other non-ensemble algorithms (MLP, LR, GNB, SVM, KNN). The Bagging-AdaBoost-SVM model had the highest AUC value (0.815 ± 0.010) on three test sets. It obtained AUC values of 0.819, 0.823, and 0.804 on test set, TCGA set and lung3 set, respectively. CONCLUSION: Our multi-dataset study showed that intensity features, texture features (GLCM, GLRLM, and GLSZM) and filtered image features were more important for distinguishing ADCs from SCCs. The method of ensemble learning can improve the prediction and generalization performance on the complicated multi-center data. The Bagging-AdaBoost-SVM model had the strongest generalization performance, and it showed promising clinical value for non-invasively predicting the histopathological subtypes of NSCLC.

RV-ESA: A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy.

Diabetic retinopathy (DR), one of the most common and serious complications of diabetes, has become one of the main blindness diseases. The retinal vasculature is the only part of the human circulatory system that allows direct noninvasive visualization of the body's microvasculature, which provides the opportunity to detect the structural and functional changes before DR becomes unable to intervene. For decades, as the fundamental step in computer-assisted analysis of retinopathy, retinal vascular extraction methods have been largely developed. However, further research focusing on retinal vascular analysis is still in its infancy. Meanwhile, due to the complexity of retinal vascular structure, the relationship between vascular geometry and DR has never been concluded. This paper aims to provide a novel computer-aided shape analysis system for retinal vessels. To perform retinal vascular shape analysis, a mathematical geometric representation is firstly generated by utilizing the proposed shape modeling method. Then, several useful statistical tools (e.g. Graph Mean, Graph PCA) are adopted to quantitatively analyze the vascular shape. Besides, in order to visualize the changes in vascular shape in the progression of DR, a geodesic tool is used to display the deformation process for ophthalmologists to observe. The efficacy of this analysis system is demonstrated in the EyePACS dataset and the subsequent visit records of 98 patients from the proprietary dataset. The experimental results show that there is a certain correlation between the variation of retinal vascular shape and DR progression, and the Graph PCA scores of retinal vessels are negatively correlated with DR grades. The code of our RV-ESA system can be publicly available at github.com/XiaolingLuo/RV-ESA.

Impact of aging on food consumption in rural China: Implications for dietary upgrading and health improvement.

Background: The issue of population aging in rural China is getting profound; nevertheless, its impact on food consumption has not been well evaluated. This study aims to examine the relationship between rural aging and family food consumption in rural China. Materials and methods: Using the statistical yearbook data and the nationally representative household-level data from the China Rural Fixed Observation Points, this study compares the evolution of food consumption between rural and urban residents from 1985 to 2020 and analyzes the structure of food consumption expenditure of rural residents. Next, this study further investigates the impact of aging on food consumption in rural households with ordinary least squares. Results: (1) The principal foods consumed by rural residents in 2020 are meat and meat products (36.8%), grain (24.5%), and vegetables (10.9%). (2) An increase in older adults has decreased the absolute consumption of all foods, while it increased relative consumption of meat and meat products, aquatic products, edible oil and fats, poultry, eggs, and sugar. (3) Due to differences in the structure of young adults' food consumption, older adults would increase their consumption of fruits and vegetables if they lived with younger adults. Conclusion: The findings of this study suggest that rural older adults may increase their consumption of fruits and vegetables by advocating intergenerational cohabitation while maintaining their intake of protein to achieve a balanced dietary structure and improve their health condition.

Multifunctional Bi NSs@BSA Nanoplatform Guided by CT Imaging for Effective Photothermal Therapy.

Photothermal therapy (PTT) has attracted great attention as an anticancer treatment strategy. With the rapid development of nanomedicine, multifunctional inorganic nanophotothermal agents provide a new way to improve the effect of PTT. Herein, bovine serum albumin (BSA)-modified Bi nanosheets (Bi NSs) with good biocompatibility were synthesized by a facile redox and ball milling method and applied as a photothermal agent for the enhancement of PTT. Owing to the strong near-infrared absorption, Bi NSs exhibit high photothermal conversion efficiency (η = 36.17%) under 808 nm laser irradiation and can serve as a nanotherapeutic agent for cancer therapy. In addition, in vitro cell safety analysis also suggests that the toxicity of BSA-modified Bi NSs is negligible. Upon 808 nm irradiation, the uptake ability of tumor cells to Bi NSs@BSA has been improved. Moreover, Bi NSs@BSA also can be used as a good contrast agent for CT imaging and then to observe the distribution of materials in the tumor site. Finally, Bi NSs@BSA-mediated PTT results show a high ablation rate of A549 tumor cells both in vitro and in vivo. All results reveal that Bi NSs@BSA is a promising nanotherapeutic platform for PTT.

X-ray triggered pea-shaped LuAG:Mn/Ca nano-scintillators and their applications for photodynamic therapy.

Photodynamic therapy (PDT) is a new minimally invasive technology for disease diagnosis and treatment. However, the biological tissue attenuation of visible light renders the depth of its penetration in tissues quite modest, which significantly restricts its therapeutic applicability. Therefore, it is an essential but yet a difficult task to enhance the X-ray sensitization impact while concurrently limiting the tissue scattering by the rational design of novel biological vectors. Herein, a novel Lu3Al5O12:Mn/Ca-Ce6@SiO2 nanoparticle system (LAMCCS) based on a pea-shaped LuAG:Mn/Ca nano-scintillator (LAMC) activating photosensitizer agent (Ce6) was designed. Due to the high radiosensitization of LAMC nano-scintillators and efficient energy conversion efficiency between LAMC and Ce6, more singlet oxygen (1O2) could be generated to efficiently damage DNA fragments and reveal a good effect of inhibiting the long-term proliferation of tumor cells in vitro. Significantly, synergistic therapy with PDT/radiotherapy (RT) and from LAMCCS nanocomposites may still maintain a high tumor growth inhibition rate of 72% than single RT of 10% in vivo. Owing to their excellent ability for X-ray sensitization and energy conversion, LAMCCS nanocomposites may have significant tumor growth suppression rates under lower X-ray dose irradiation due to their outstanding X-ray sensitization and energy conversion capabilities, which may open up a new avenue for the advancement of cancer therapy.

The Coordinated Development and Regulation Research on Public Health, Ecological Environment and Economic Development: Evidence from the Yellow River Basin of China.

The dual problems of the public crisis from the global epidemic and the deterioration of the ecological environment constrain the economic development in the Yellow River Basin. To promote the sustainable and balanced development in the Yellow River Basin, this paper takes public health, ecological environment, and economic development, as a whole, to study the coordinated development of the Yellow River Basin. Based on coupling coordinated theory, we use the SMI-P method to evaluate the coordinated development index of public health, the ecological environment, and economic development in the Yellow River Basin. Moreover, we use the coordinated regulation and obstacle factor diagnosis to identify the main influencing factors and design regulation methods to optimize the coordinated development index. The results found that (1), during the research period, there is spatiotemporal heterogeneity in the coordinated development level in the Yellow River Basin. From 2009 to 2019, the overall development index increased steadily, while the regional disparity in the coordinated development level was obvious. (2) The ecological environment indicators contribute more to the relevance and obstacle factors, such as the average concentration of fine particulate matter, per capita arable land area, afforestation area, etc. (3) After regulating the overall development level of the Yellow River Basin, we prove that Path 4, which comprehensively considers the relevance and obstacle factors, performs better.

A multi-classification model for non-small cell lung cancer subtypes based on independent subtask learning.

PURPOSE: The non-small cell lung cancer (NSCLC) can be divided into adenocarcinoma (ADC), squamous cell carcinoma (SCC), large cell carcinoma (LCC), and not otherwise specified (NOS), which is crucial for clinical decision-making. However, current related researches are rare for the complex multi-classification of NSCLC, mainly due to the serious data imbalance, the difficulty to unify the feature space, and the complicated decision boundary among multiple subtypes. The machine learning method of traditional "one-vs-one" (OVO) is difficult to solve these problems and achieve good results. METHODS: To this end, we propose a novel independent subtask learning (ISTL) method to better carry out the multi-classification task. Specifically, it includes four pertinent strategies: (1) independent data expansion; (2) independent feature selection (IFS); (3) independent model construction; and (4) a novel voting strategy: majority voting combined with Bayesian prior. We performed experiments using 1036 CT scans (ADC:SCC:LCC:NOS = 600:268:105:63) collected from eight international databases, and the data acquisition was highly complex and diverse. RESULTS: The experimental results showed that the ISTL method obtained an accuracy of 0.812 on the independent test cohort, which significantly improved the performance of multi-classification compared with the traditional OVO-support vector machine (0.691) and OVO-random forest (0.710) models. After the IFS, six selected feature sets of six binary tasks are obviously different, indicating that the ISTL method has better interpretability to distinguish the multiple NSCLC subtypes. The results of a further auxiliary contrast experiment showed that four pertinent strategies were all effective. CONCLUSION: Our work indicates that the ISTL method can effectively perform multi-classification of NSCLC subtypes with better interpretability for clinical computer-aided detection and has the potential to be applied in a wide range of multi-classification studies.