PANX2 promotes malignant transformation of colorectal cancer and 5-Fu resistance through PI3K-AKT signaling pathway.

Colorectal cancer (CRC) is the third deadliest cancer in the world, with a high incidence, aggressiveness, poor prognosis, and resistant to drugs. 5-fluorouracil (5-FU) is the most commonly used drug for the chemotherapeutic of CRC, however, CRC is resistant to 5-FU after a period of treatment. Therefore, there is an urgent need to explore the underlying molecular mechanisms of CRC resistance to 5-FU. In the present study, we found that the expression of PANX2 was increased in CRC tissues and metastatic tissues from the TCGA database. The K-M survival curve showed that the high expression of PANX2 was associated with poor cancer prognosis. GDSC database showed that the IC50 of 5-Fu in the PANX2 high expression group was significantly higher, and the results were verified in CRC cells. In vitro cell function and in vivo tumorigenesis experiments showed that PANX2 promoted CRC cell proliferation, clone formation, migration and tumorigenesis in vivo. WB result revealed that PANX2 may lead to resistance to 5-Fu in CRC by affecting the PI3K-AKT signaling pathway. Overall, PANX2 regulates CRC proliferation, clone formation, migration, and 5-Fu resistance by PI3K-AKT signaling pathway.

Polar and quasicrystal vortex observed in twisted-bilayer molybdenum disulfide.

We report the observation of an electric field in twisted-bilayer molybdenum disulfide (MoS2) and elucidate its correlation with local polar domains using four-dimensional scanning transmission electron microscopy (4D-STEM) and first-principles calculations. We reveal the emergence of in-plane topological vortices within the periodic moiré patterns for both commensurate structures at small twist angles and the incommensurate quasicrystal structure that occurs at a 30° twist. The large-angle twist leads to mosaic chiral vortex patterns with tunable characteristics. A twisted quasicrystal bilayer, characterized by its 12-fold rotational symmetry, hosts complex vortex patterns and can be manipulated by picometer-scale interlayer displacement. Our findings highlight that twisting 2D bilayers is a versatile strategy for tailoring local electric polar vortices.

Systemic immunoinflammatory index and prognostic nutrition index for predicting pathologic responses of patients with advanced gastric cancer after neoadjuvant therapy for advanced gastric cancer.

To investigate the value of prognostic nutrition index (PNI) and systemic immunoinflammatory index (SII) for predicting pathological responses of patients with advanced gastric cancer (GC) after neo-adjuvant chemotherapy (NACT). The clinicopathological data of 326 patients with advanced GC who received NACT in Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City) from January 2017 to December 2021 were retrospectively collected. The SII and PNI of patients were calculated. The receiver operating characteristics (ROC) curve was leveraged for getting the optimal cutoff values of SII and PNI. The pathological response of patients after NACT, as obtained from their postoperative pathological examinations, was evaluated based on the tumor regression grade (TRG) criteria. Multivariate regression analysis was employed for identifying factors that led to various pathological responses after NACT in advanced GC patients. The log-rank test was utilized for between-group comparison of patients' survival curves. The SII and PNI were 507.45 and 48.48 respectively, and their levels were divided into high and low groups. Pathological response (TRG 0-1) was observed in 66 cases (20.25%), while non-pathological response (TRG 2-3) was observed in 260 cases (79.75%). The results of multivariate logistic regression analysis showed that tumor diameter < 5 cm, ypT T0-T2, ypN N0, chemotherapy regimen XELOX (capecitabine combined with oxaliplatin), SII < 507.45 (P=0.002), PNI > 48.48 were all independent factors affecting the pathological responses of advanced GC patients after NACT (all P < 0.05). With SII and PNI being included, the AUC was 0.821 (95% CI: 0.765-0.876), and the specificity was 87.90% and the sensitivity was 64.20%. The Kaplan-Meier survival curve analysis showed that NACT patients with tumor diameter < 5 cm, ypT T0-T2, ypN N0, XELOX chemotherapy regimen, SII < 507.45 and SII ≥ 507.45 had a higher survival rate. (P < 0.001). Before treatment, tumor diameter < 5 cm, ypT T0-T2, ypN N0, chemotherapy regimen XELOX, SII < 507.45, PNI > 48.48 were all independent factors affecting the pathological response of advanced GC patients after NACT. Moreover, the inclusion of SII and PNI increased the accuracy of predicting the pathological response of patients after NACT.

A Multi-Strategy Improved Northern Goshawk Optimization Algorithm for Optimizing Engineering Problems.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO's exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.

Optimizing Deep Learning Models with Improved BWO for TEC Prediction.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

wgatools: an ultrafast toolkit for manipulating whole genome alignments.

Summary: With the rapid development of long-read sequencing technologies, the era of individual complete genomes is approaching. We have developed wgatools, a cross-platform, ultrafast toolkit that supports a range of whole genome alignment (WGA) formats, offering practical tools for conversion, processing, statistical evaluation, and visualization of alignments, thereby facilitating population-level genome analysis and advancing functional and evolutionary genomics. Availability and Implementation: wgatools supports diverse formats and can process, filter, and statistically evaluate alignments, perform alignment-based variant calling, and visualize alignments both locally and genome-wide. Built with Rust for efficiency and safe memory usage, it ensures fast performance and can handle large datasets consisting of hundreds of genomes. wgatools is published as free software under the MIT open-source license, and its source code is freely available at https://github.com/wjwei-handsome/wgatools.

Amyotrophic lateral sclerosis associated with Sjögren's syndrome: a case report.

BACKGROUND: Motor neuron disease (MND) is a chronic and progressive neurodegenerative disorder with an unknown cause. The development of amyotrophic lateral sclerosis (ALS) is believed to be linked to an immune response. Monocytes/macrophages and T cells are key players in the disease's advancement. Monitoring levels of cytokines in the blood can help forecast patient outcomes, while immunotherapy shows promise in alleviating symptoms for certain individuals. CASE PRESENTATION: A 56-year-old male patient was admitted to the hospital due to progressive limb weakness persisting for eight months. The neurological examination revealed impairments in both upper and lower motor neurons, as well as sensory anomalies, without corresponding signs. Electrophysiological examination results indicated extensive neuronal damage and multiple peripheral nerve impairments, thereby the diagnosis was ALS. One month ago, the patient began experiencing symptoms of dry mouth and a bitter taste. Following tests for rheumatic immune-related antibodies and a lip gland biopsy, a diagnosis of Sjögren's syndrome (SS) was proposed. Despite treatment with medications such as hormones (methylprednisolone), immunosuppressants (hydroxychloroquine sulfate), and riluzole, the symptoms did not significantly improve, but also did not worsen. CONCLUSION: It is recommended to include screening for SS in the standard assessment of ALS. Furthermore, research should focus on understanding the immune mechanisms involved in ALS, providing new insights for the diagnosis and treatment of ALS in conjunction with SS.

Effects of thermophilic and acidophilic microbial consortia on maize wet-milling steeping.

To understand the ecology of species and promote biotechnology through beneficial strain selection for improving starch yield in maize wet-milling steeping, bacterial diversity and community structure during the counter-current steeping process in a commercial steeping system were characterized and investigated. The microbial diversity in the steeping liquor, which consisted of 16 phyla, 131 families, and 290 genera, was more abundant compared to those present on the surface of unsteeped maize. As the counter-current steeping progressed, exposing newer maize to the older steepwater, Lactobacillus dominated, replacing Rahnella, Pseudomonas, Pantoea, and Serratia. The thermophilic and acidophilic microbial consortia were enriched through adaptive evolution engineering and employed to improve starch yield. Several steeping strategies were evaluated, including water alone, SO2 alone, mono-culture of B. coagulans, microbial consortia, and a combination of consortium and SO2. Combining the microbial consortium with SO2 significantly increased the starch yield to, about 66.4 ± 0.5%, a 22% and 46% increase over SO2 alone and the consortium alone, respectively. Scanning electron microscope (SEM) of steeped maize structure indicated that the combination of consortium and SO2 disrupted the protein matrix and widened gaps between starch granules in maize endosperm. This released proteins into the steepwater and left starch granules in the aleurone layer. The steeping strategy of using thermophilic and acidophilic microbial consortium as additives shows potential application as an environmentally friendly alternative to conventional maize steeping procedures.

Beyond the standard GWAS - a guide for plant biologists.

Classic genome-wide association studies (GWAS) look for associations between individual SNPs and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits, or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

IGF-1's protective effect on OSAS rats' learning and memory.

PURPOSE: Patients with obstructive sleep apnea syndrome (OSAS) frequently experience cognitive dysfunction, which may be connected to chronic intermittent hypoxia (CIH). Insulin-like growth factor-1 (IGF-1) is thought to be closely associated with cognitive function, but its role in cognitive impairment caused by OSAS is unclear. The purpose of this study was to investigate the potential protective effect of IGF-1 on cognitive impairment in OSAS rats. METHODS: Healthy male SD rats (n = 40) were randomly assigned into four groups: control group, CIH group, NS + CIH group, and IGF-1 + CIH group. All experimental rats except for those in the control group were exposed to intermittent hypoxic (IH) environments for 8 h per day over 28 days. Prior to daily exposure to IH, rats in the IGF-1 + CIH group received subcutaneous injections of IGF-1. The Morris water maze test was conducted on all experimental rats. Brain tissue testing methods included Enzyme-Linked Immunosorbent Assay, Hematoxylin and eosin staining, Immunohistochemistry, and Western blotting. RESULTS: The rat model of OSAS was successfully established following exposure to CIH and exhibited significant cognitive impairment. However, daily subcutaneous injections of IGF-1 partially restored the impaired cognitive function in OSAS rats. Compared with the control group, there was a significant decrease in the expression levels of IGF-1, p-IGF-IR, and SYP in the CIH group; however, these expression levels increased significantly in the IGF-I + CIH group. CONCLUSION: In OSAS rats, IGF-1 enhances learning memory; this effect may be linked to increased p-IGF-1R and SYP protein production in the hippocampus.

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions.

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Effects of Nitrogen Application at Different Levels by a Sprinkler Fertigation System on Crop Growth and Nitrogen-Use Efficiency of Winter Wheat in the North China Plain.

Nitrogen (N) is an essential macronutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat (Triticum aestivum L.) is one of the main food crops, and its yield has increased from approximately 4000 kg ha-1 to 6000 kg ha-1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha-1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017-2018 and 2018-2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertilizer-utilization efficiency and on the crop growth and yield of winter wheat under sprinkler fertigation conditions. The results showed that in the N application range of 40-200 kg ha-1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate (from N200 to N250) did not have additional benefits. The N uptake after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the recommended optimal N application rate was 200 kg ha-1, and the best topdressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions.

Constructing Stable Polyvinyl Alcohol/Gelatin/Cellulose Nanocrystals Composite Electrospun Membrane with Excellent Filtration Efficiency for PM2.5.

Considering the high demand for air quality, the development of biomass-based air filtration membranes with high air filtration efficiency and good stability is an urgent task. In this work, polyvinyl alcohol (PVA), gelatin (GA), and cellulose nanocrystals (CNC) were mixed and prepared into a membrane through an electrospinning method for air filtration. After a hydrophobic modification, the modified PVA/GA/CNC composite membrane showed excellent filtration efficiency for PM2.5 (97.65%) through the internal three-dimensional structure barrier and the electrostatic capture effect of the CNC with a negative charge, as well as a low-pressure drop (only 50 Pa). In addition, the modified PVA/GA/CNC composite membrane had good mechanical properties (maximum tensile fracture rate of 78.3%) and high stability (air filtration efficiency of above 90% after five wash-filter cycles and a high-temperature treatment at 200 °C). It is worth noting that the whole preparation process is completed without organic solvents, putting forward a new strategy for the construction of green air filtration membranes.

Altered excitation energy transfer between phycobilisome and photosystems in the absence of ApcG, a small linker peptide, in Synechocystis sp. PCC 6803, a cyanobacterium.

Phycobilisome (PBS) is a large pigment-protein complex in cyanobacteria and red algae responsible for capturing sunlight and transferring its energy to photosystems (PS). Spectroscopic and structural properties of various PBSs have been widely studied, however, the nature of so-called complex-complex interactions between PBS and PSs remains much less explored. In this work, we have investigated the function of a newly identified PBS linker protein, ApcG, some domain of which, together with a loop region (PB-loop in ApcE), is possibly located near the PBS-PS interface. Using Synechocystis sp. PCC 6803, we generated an ApcG deletion mutant and probed its deletion effect on the energetic coupling between PBS and photosystems. Steady-state and time-resolved spectroscopic characterization of the purified ΔApcG-PBS demonstrated that ApcG removal weakly affects the photophysical properties of PBS for which the spectroscopic properties of terminal energy emitters are comparable to those of PBS from wild-type strain. However, analysis of fluorescence decay imaging datasets reveals that ApcG deletion induces disruptions within the allophycocyanin (APC) core, resulting in the emergence (splitting) of two spectrally diverse subgroups with some short-lived APC. Profound spectroscopic changes of the whole ΔApcG mutant cell, however, emerge during state transition, a dynamic process of light scheme adaptation. The mutant cells in State I show a substantial increase in PBS-related fluorescence. On the other hand, global analysis of time-resolved fluorescence demonstrates that in general ApcG deletion does not alter or inhibit state transitions interpreted in terms of the changes of the PSII and PSI fluorescence emission intensity. The results revealed yet-to-be discovered mechanism of ApcG-docking induced excitation energy transfer regulation within PBS or to Photosystems.

Metabolomic reveals the responses of sludge properties and microbial communities to high nitrite stress in denitrifying phosphorus removal systems.

Nitrite, as an electron acceptor, plays a good role in denitrifying phosphorus removal (DPR); however, high nitrite concentration has adverse affects on sludge performance. We investigated the precise mechanisms of responses of sludge to high nitrite stress, including surface characteristics, intracellular and extracellular components, microbial and metabolic responses. When the nitrite stress reached 90 mg/L, the sludge settling performance was improved, but the activated sludge was aging. FTIR and XPS analysis revealed a significant increase in the hydrophobicity of the sludge, resulting in improve settling performance. However, the intracellular carbon sources synthesis was inhibited. In addition, the components in the tightly bound extracellular polymeric substances (TB-EPS) of sludge were significantly reduced and indicated the disturb of metabolism. Notably, Exiguobacterium emerged as a new genus when face high nitrite stress that could maintaining survival in hostile environments. Moreover, metabolomic analysis demonstrated strong biological response to nitrite stress further supported above results that include the inhibited of carbohydrate and amino acid metabolism. More importantly, some lipids (PS, PA, LysoPA, LysoPC and LysoPE) were significantly upregulated that related enhanced membrane lipid remodeling. The comprehensive analyses provide novel insights into the high nitrite stress responses mechanisms in activated sludge systems.

Unlocking growth potential in Halomonas bluephagenesis for enhanced PHA production with sulfate ions.

The mutant strain Halomonas bluephagenesis (TDH4A1B5P) was found to produce PHA under low-salt, non-sterile conditions, but the yield was low. To improve the yield, different nitrogen sources were tested. It was discovered that urea was the most effective nitrogen source for promoting growth during the stable stage, while ammonium sulfate was used during the logarithmic stage. The growth time of H. bluephagenesis (TDH4A1B5P) and its PHA content were significantly prolonged by the presence of sulfate ions. After 64 hr in a 5-L bioreactor supplemented with sulfate ions, the dry cell weight (DCW) of H. bluephagenesis weighed 132 g/L and had a PHA content of 82%. To promote the growth and PHA accumulation of H. bluephagenesis (TDH4A1B5P), a feeding regimen supplemented with nitrogen sources and sulfate ions with ammonium sodium sulfate was established in this study. The DCW was 124 g/L, and the PHA content accounted for 82.3% (w/w) of the DCW, resulting in a PHA yield of 101 g/L in a 30-L bioreactor using the optimized culture strategy. In conclusion, stimulating H. bluephagenesis (TDH4A1B5P) to produce PHA is a feasible and suitable strategy for all H. bluephagenesis.

Clean Transfer of Two-Dimensional Materials: A Comprehensive Review.

The growth of two-dimensional (2D) materials through chemical vapor deposition (CVD) has sparked a growing interest among both the industrial and academic communities. The interest stems from several key advantages associated with CVD, including high yield, high quality, and high tunability. In order to harness the application potentials of 2D materials, it is often necessary to transfer them from their growth substrates to their desired target substrates. However, conventional transfer methods introduce contamination that can adversely affect the quality and properties of the transferred 2D materials, thus limiting their overall application performance. This review presents a comprehensive summary of the current clean transfer methods for 2D materials with a specific focus on the understanding of interaction between supporting layers and 2D materials. The review encompasses various aspects, including clean transfer methods, post-transfer cleaning techniques, and cleanliness assessment. Furthermore, it analyzes and compares the advances and limitations of these clean transfer techniques. Finally, the review highlights the primary challenges associated with current clean transfer methods and provides an outlook on future prospects.

PSE-Net: Channel pruning for Convolutional Neural Networks with parallel-subnets estimator.

Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverages neural architecture search to directly find networks with a configurable width, the key step of which is to identify representative subnet for various pruning ratios by training a supernet. However, current methods mainly follow a serial training strategy to optimize supernet, which is very time-consuming. In this work, we introduce PSE-Net, a novel parallel-subnets estimator for efficient channel pruning. Specifically, we propose a parallel-subnets training algorithm that simulate the forward-backward pass of multiple subnets by droping extraneous features on batch dimension, thus various subnets could be trained in one round. Our proposed algorithm facilitates the efficiency of supernet training and equips the network with the ability to interpolate the accuracy of unsampled subnets, enabling PSE-Net to effectively evaluate and rank the subnets. Over the trained supernet, we develop a prior-distributed-based sampling algorithm to boost the performance of classical evolutionary search. Such algorithm utilizes the prior information of supernet training phase to assist in the search of optimal subnets while tackling the challenge of discovering samples that satisfy resource constraints due to the long-tail distribution of network configuration. Extensive experiments demonstrate PSE-Net outperforms previous state-of-the-art channel pruning methods on the ImageNet dataset while retaining superior supernet training efficiency. For example, under 300M FLOPs constraint, our pruned MobileNetV2 achieves 75.2% Top-1 accuracy on ImageNet dataset, exceeding the original MobileNetV2 by 2.6 units while only cost 30%/16% times than BCNet/AutoAlim.

Multi-Modal Enhancement Transformer Network for Skeleton-Based Human Interaction Recognition.

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods.

The estrogen receptor and metabolism.

Across the globe, metabolic syndrome, hyperuric acid, and their related diseases, such as cardiovascular disease, diabetes, and insulin resistance, are increasing in incidence due to metabolic imbalances. Due to the pathogenesis, women are more prone to these diseases than men. As estrogen levels decrease after menopause, obesity and metabolic disorders are more likely to occur. Men are also affected by hyperuric acid. To provide ideas for the prevention and treatment of metabolic syndrome and hyperuricemia, this article reviews and analyzes the relationship between estrogen receptors, metabolic syndrome, and hyperuricemia.

Influence of estrogen receptor on metabolic syndrome and hyperuricemiaA narrative review discusses the mechanism of estrogen and estrogen receptors for metabolic syndrome and hyperuric acid, and highlights the important for prevention and treatment of metabolic balances.