Coal-derived boron and phosphorus co-doped activated carbon with expanded interlayer space for high performance sodium ion capacitor anode.

Aiming at the key problem of Na+ insertion difficulty and low charge transfer efficiency of activated carbon materials. It is an effective strategy to increase the lattice spacing and defect concentration by doping to reduce the ion diffusion resistance and improve the kinetics. Hence, anthracitic coal is used to prepare activated carbon (AC) and B,P-doped activated carbon (B,P-AC) as the cathode and anode materials for high-performance all-carbon SICs, respectively. AC cathode material has high specific surface area and reasonable micropore structure, which shows excellent capacitance performance. B,P-AC anode material has the advantages of extremely high specific surface area (1856.1 m2/g), expanded interlayer spacing (0.40 nm) and uniform distribution of B and P heteroatoms. Hence, B,P-AC anode achieves a highly reversible Na+ storage capacity of 243 mAh/g at a current density of 0.05 A/g. Density functional theory (DFT) calculations further verify that B,P-AC has stronger Na+ storage performance. The final assembled B,P-AC//AC SIC offers a high energy density of 109.78 Wh kg-1 and a high-power density of 10.03 kW kg-1. The high-performance coal-derived activated carbon of this work provides a variety of options for industrial production of electrode materials for sodium ion capacitors.

Peroxynitrite scavenger FeTPPS binds with hCT to effectively inhibit its amyloid aggregation.

Human calcitonin (hCT) is an endogenous polypeptide commonly employed in treating bone resorption-related illnesses, but its clinical application is limited due to its high aggregation tendency. Metalloporphyrins are effective in suppressing amyloid fibrillation, positioning them as potential drug candidates for amyloidogenic disorders like Alzheimer's and type 2 diabetes. In this work, we investigated the effects of Fe(III) meso-tetra(4-sulfonatophenyl)porphine chloride (FeTPPS), a highly efficient ONOO- decomposition catalyst, on hCT aggregation. Our findings reveal that FeTPPS effectively precludes hCT fibrillation by stabilizing the monomers and delaying the structural transition from α-helix bundles to ß-sheet-rich aggregates. The macrocyclic ring of FeTPPS plays a significant role in disrupting hCT self-associations. Among various porphyrin analogs, those with an iron center and negatively charged peripheral substituents exhibit a stronger inhibitory effect on hCT aggregation. Spectroscopic analyses and computational simulations indicate that FeTPPS binds to hCT's core aggregation region via complexation with His20 in a 1 : 1 molar ratio. Hydrophobic interaction, hydrogen bonding, and π-π stacking with the residues involving Tyr12, Phe19, and Ala26 also contribute to the interactions. Collectively, our study provides a promising approach for developing novel hCT drug formulations and offers theoretical guidance for designing metalloporphyrin-based inhibitors for various amyloidosis conditions.

PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.

The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an encoder-decoder structure. This model's high precision in identifying binding sites significantly advances the field of computational biology, offering vital insights for peptide drug development and protein science.

A Paradoxical Tumor Antigen Specific Response in the Liver.

Functional tumor-specific CD8+ T cells are essential for an effective anti-tumor immune response and the efficacy of immune checkpoint inhibitor therapy. In comparison to other organ sites, we found higher numbers of tumor-specific CD8+ T cells in primary, metastatic liver tumors in murine tumor models. Despite their abundance, CD8+ T cells in the liver displayed an exhausted phenotype. Depletion of CD8+ T cells showed that liver tumor-reactive CD8+ T failed to control liver tumors but was effective against subcutaneous tumors. Similarly, analysis of single-cell RNA sequencing data from patients showed a higher frequency of exhausted tumor-reactive CD8+ T cells in liver metastasis compared to paired primary colon cancer. High-dimensional, multi-omic analysis combining proteomic CODEX and scRNA-seq data revealed enriched interaction of SPP1+ macrophages and CD8+ tumor-reactive T cells in profibrotic, alpha-SMA rich regions in the liver. Liver tumors grew less in Spp1 -/- mice and the tumor-specific CD8+ T cells were less exhausted. Differential pseudotime trajectory inference analysis revealed extrahepatic signaling promoting an intermediate cell (IC) population in the liver, characterized by co-expression of VISG4, CSF1R, CD163, TGF-ßR, IL-6R, SPP1. scRNA-seq of a third data set of premetastatic adenocarcinoma showed that enrichment of this population may predict liver metastasis. Our data suggests a mechanism by which extrahepatic tumors facilitate the formation of liver metastasis by promoting an IC population inhibiting tumor-reactive CD8+ T cell function.

Highly sensitive detection platform-based diagnosis of oesophageal squamous cell carcinoma in China: a multicentre, case-control, diagnostic study.

BACKGROUND: Early detection and screening of oesophageal squamous cell carcinoma rely on upper gastrointestinal endoscopy, which is not feasible for population-wide implementation. Tumour marker-based blood tests offer a potential alternative. However, the sensitivity of current clinical protein detection technologies is inadequate for identifying low-abundance circulating tumour biomarkers, leading to poor discrimination between individuals with and without cancer. We aimed to develop a highly sensitive blood test tool to improve detection of oesophageal squamous cell carcinoma. METHODS: We designed a detection platform named SENSORS and validated its effectiveness by comparing its performance in detecting the selected serological biomarkers MMP13 and SCC against ELISA and electrochemiluminescence immunoassay (ECLIA). We then developed a SENSORS-based oesophageal squamous cell carcinoma adjunct diagnostic system (with potential applications in screening and triage under clinical supervision) to classify individuals with oesophageal squamous cell carcinoma and healthy controls in a retrospective study including participants (cohort I) from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China), Henan Cancer Hospital (HNCH; Zhengzhou, China), and Cancer Hospital of Shantou University Medical College (CHSUMC; Shantou, China). The inclusion criteria were age 18 years or older, pathologically confirmed primary oesophageal squamous cell carcinoma, and no cancer treatments before serum sample collection. Participants without oesophageal-related diseases were recruited from the health examination department as the control group. The SENSORS-based diagnostic system is based on a multivariable logistic regression model that uses the detection values of SENSORS as the input and outputs a risk score for the predicted likelihood of oesophageal squamous cell carcinoma. We further evaluated the clinical utility of the system in an independent prospective multicentre study with different participants selected from the same three institutions. Patients with newly diagnosed oesophageal-related diseases without previous cancer treatment were enrolled. The inclusion criteria for healthy controls were no obvious abnormalities in routine blood and tumour marker tests, no oesophageal-associated diseases, and no history of cancer. Finally, we assessed whether classification could be improved by integrating machine-learning algorithms with the system, which combined baseline clinical characteristics, epidemiological risk factors, and serological tumour marker concentrations. Retrospective SYSUCC cohort I (randomly assigned [7:3] to a training set and an internal validation set) and three prospective validation sets (SYSUCC cohort II [internal validation], HNCH cohort II [external validation], and CHSUMC cohort II [external validation]) were used in this step. Six machine-learning algorithms were compared (the least absolute shrinkage and selector operator regression, ridge regression, random forest, logistic regression, support vector machine, and neural network), and the best-performing algorithm was chosen as the final prediction model. Performance of SENSORS and the SENSORS-based diagnostic system was primarily assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). FINDINGS: Between Oct 1, 2017, and April 30, 2020, 1051 participants were included in the retrospective study. In the prospective diagnostic study, 924 participants were included from April 2, 2022, to Feb 2, 2023. Compared with ELISA (108·90 pg/mL) and ECLIA (41·79 pg/mL), SENSORS (243·03 fg/mL) showed 448 times and 172 times improvements, respectively. In the three retrospective validation sets, the SENSORS-based diagnostic system achieved AUCs of 0·95 (95% CI 0·90-0·99) in the SYSUCC internal validation set, 0·93 (0·89-0·97) in the HNCH external validation set, and 0·98 (0·97-1·00) in the CHSUMC external validation set, sensitivities of 87·1% (79·3-92·3), 98·6% (94·4-99·8), and 93·5% (88·1-96·7), and specificities of 88·9% (75·2-95·8), 74·6% (61·3-84·6), and 92·1% (81·7-97·0), respectively, successfully distinguishing between patients with oesophageal squamous cell carcinoma and healthy controls. Additionally, in three prospective validation cohorts, it yielded sensitivities of 90·9% (95% CI 86·1-94·2) for SYSUCC, 84·8% (76·1-90·8) for HNCH, and 95·2% (85·6-98·7) for CHSUMC. Of the six machine-learning algorithms compared, the random forest model showed the best performance. A feature selection step identified five features to have the highest performance to predictions (SCC, age, MMP13, CEA, and NSE) and a simplified random forest model using these five features further improved classification, achieving sensitivities of 98·2% (95% CI 93·2-99·7) in the internal validation set from retrospective SYSUCC cohort I, 94·1% (89·9-96·7) in SYSUCC prospective cohort II, 88·6% (80·5-93·7) in HNCH prospective cohort II, and 98·4% (90·2-99·9) in CHSUMC prospective cohort II. INTERPRETATION: The SENSORS system facilitates highly sensitive detection of oesophageal squamous cell carcinoma tumour biomarkers, overcoming the limitations of detecting low-abundance circulating proteins, and could substantially improve oesophageal squamous cell carcinoma diagnostics. This method could act as a minimally invasive screening tool, potentially reducing the need for unnecessary endoscopies. FUNDING: The National Key R&D Program of China, the National Natural Science Foundation of China, and the Enterprises Joint Fund-Key Program of Guangdong Province. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Structure Evolution and Optical Tuning of One-Dimensional Post-perovskite (TDMP)PbBr4 under High Pressure.

Optimizing the structure and tuning the optical properties in low-dimensional organic-inorganic halide perovskites are crucial to practical applications for stable solid-state lighting. Herein, we performed high-pressure investigations on one-dimensional (1D) postperovskite (TDMP)PbBr4 (TDMP = trans-2,5-dimethylpiperaziniium), and structure and optical properties under pressure are studied. (TDMP)PbBr4 exhibits color tunable emission from cool white light to yellow orange as the pressure increases from atmospheric pressure to 20.0 GPa. It was found that high pressure would facilitate trapping the free exciton (free exciton) to form a self-trapped exciton (STE) state due to increased electron-phonon interaction, thus enhancing STE emission in the pressure range of 4.0-7.0 GPa. At above 7.0 GPa, the STE emission is quenched, which is due to the phonon-assisted nonradiative relaxation. Meanwhile, (TDMP)PbBr4 displays reversible piezochromism from colorless to yellow under pressure as a result of the compound undergoing a reversible structural transformation. This work provides an insightful perspective on revealing the relationship between structure and optical properties of 1D postperovskites under high pressure.

In-situ synthesis to promote surface reconstruction of metal-organic frameworks for high-performance water/seawater oxidation.

Metal-organic frameworks (MOFs) have gained tremendous notice for the application in alkaline water/seawater oxidation due to their tunable structures and abundant accessible metal sites. However, exploring cost-effective oxygen evolution reaction (OER) electrocatalysts with high catalytic activity and excellent stability remains a great challenge. In this work, a promising strategy is proposed to regulate the crystalline structures and electronic properties of NiFe-metal-organic frameworks (NiFe-MOFs) by altering the organic ligands. As a representative sample, NiFe-BDC (BDC: C8H6O4) synthesized on nickel foam (NF) shows extraordinary OER activity in alkaline condition, delivering ultralow overpotentials of 204, 234 and 273 mV at 10, 100, and 300 mA cm-2, respectively, with a small Tafel slope of 21.6 mV dec-1. Only a slight decrease is observed when operating in alkaline seawater. The potential attenuation is barely identified at 200 mA cm-2 over 200 h continuous test, indicating the remarkable stability and corrosion resistance. In-situ measurements indicate that initial Ni2+/Fe2+ goes through oxidation process into Ni3+/Fe3+ during OER, and eventually presents in the form of NiFeOOH/NiFe-BDC heterojunction. The unique self-reconstructed surface is responsible for the low reaction barrier and fast reaction kinetics. This work provides an effective strategy to develop efficient MOF-based electrocatalysts and an insightful view on the dynamic structural evolution during OER.

Effects of dexmedetomidine on renal function, inflammatory markers, and cognitive functioning in elderly patients undergoing hip replacement surgery.

OBJECTIVE: To investigate the effects of dexmedetomidine on renal function, inflammatory markers, and cognitive outcome, and to identify factors influencing early postoperative cognitive dysfunction (POCD) in elderly patients undergoing hip replacement surgery. METHODS: A retrospective analysis was conducted on 162 elderly patients who underwent hip replacement surgery at Cangzhou Central Hospital from March 2022 to May 2023. Patients were divided into a control group (without dexmedetomidine) and an experimental group (with dexmedetomidine). Measurements included creatinine (Cr), blood urea nitrogen (BUN), interleukin 1ß (IL-1ß), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), Montreal Cognitive Assessment (MoCA) score, and the incidence of POCD seven days postoperatively. Univariate and logistic regression analyses were employed to investigate the predictors of early POCD. RESULTS: Significant differences were observed between the groups in terms of renal function, inflammatory markers, and cognitive outcome (Cr, BUN, IL-1ß, TNF-α, IL-6 and MoCA scores) (all P<0.05). The experimental group showed a significantly lower incidence of POCD at seven days post-surgery (P<0.05). Logistic regression identified having a neuron-specific enolase (NSE) level seven days post-surgery ≥7.0 pg/ml as a risk factor for early POCD (P=0.001, OR=3.987, 95% CI: 1.789-8.886), whereas intraoperative use of dexmedetomidine was a protective factor (P=0.041, OR=0.424, 95% CI: 0.187-0.964). CONCLUSION: The use of dexmedetomidine in hip replacement surgery can mitigate postoperative renal injury and inflammatory response, enhance cognitive outcome, and significantly reduce the incidence and risk of early POCD in elderly patients.

Protective Effects of Gastrodin Against Gentamicin-Induced Vestibular Damage by the Notch Signaling Pathway.

PURPOSE: Gentamicin is a broad-spectrum antibiotic commonly used in clinical practice. However, the drug causes side effects of ototoxicity, leading to disruption in balance functionality. This study investigated the effect of gastrodin, a prominent compound present in Gastrodia, and the underlying mechanism on the development of gentamicin-induced vestibular dysfunction. METHODS: Wild-type C57BL/6 mice were randomly assigned to three groups: control, gentamicin, and gentamicin + gastrodin groups. The extent of gentamicin-induced vestibular impairment was assessed through a series of tests including the swimming test, contact righting reflex test, and air-righting reflex. Alterations in vestibular hair cells were monitored through immunofluorescence assay, and cellular apoptosis was observed using TUNEL staining. The mRNA and protein expression of Notch1, Jagged1, and Hes1 was quantified through qRT-PCR, immunofluorescence, and western blot analyses. RESULTS: Gentamicin treatment led to pronounced deficits in vestibular function and otolith organ hair cells in mice. Nevertheless, pretreatment with gastrodin significantly alleviated these impairments. Additionally, the Notch signaling pathway was activated by gentamicin in the utricle, contributing to a notable increase in the expression levels of apoptosis-associated proteins. By contrast, gastrodin treatment effectively suppressed the Notch signaling pathway, thereby mitigating the occurrence of apoptosis. CONCLUSION: Collectively, these findings underscore the crucial role of gastrodin in safeguarding against gentamicin-induced vestibular dysfunction through the modulation of the Notch signaling pathway. This study suggests the potential of gastrodin as a promising therapeutic agent for preventing vestibular injuries.

The Epstein-Barr virus small capsid protein BFRF3 disrupts the NF-кB signaling pathway by inhibiting p65 activity.

Epstein-Barr virus (EBV), a common gamma herpesvirus, establishes a life-long latent infection in the host to defend against innate immune recognition, which is closely related to a variety of malignant tumors, but its specific mechanism is unclear. BFRF3, an EBV-encoded small capsid protein, is mainly involved in the assembly of the viral capsid structure and the maintenance of its stability. Here, we showed that BFRF3 can inhibit TNF-α-mediated NF-кB promoter activation. Moreover, BFRF3 downregulates NF-кB-mediated promoter activation and transcription of inflammatory cytokines, including IL-6 and IL-8. Dual-luciferase reporter assay demonstrated that BFRF3 restrains NF-кB promoter activity at or below the p65 level, and coimmunoprecipitation analysis revealed that BFRF3 not only interacts with p65 but also binds to its critical truncated Rel homology domain (RHD) and transcriptional activation domain (TAD). However, BFRF3 does not affect the dimerization of p65-p50, but overexpression of BFRF3 reduces the nuclear accumulation of p65, and the phosphorylation of p65 (Ser536) is repressed during BFRF3 transfection and EBV lytic infection, which promotes the proliferation of EBV. Overall, our study suggested that BFRF3 may play a crucial role in antiviral immunity to defend against EBV infection by inhibiting NF-κB activity.

Navigating therapeutic challenges in VEXAS syndrome: exploring IL-6 and JAK inhibitors at the forefront.

VEXAS syndrome, an uncommon yet severe autoimmune disorder stemming from a mutation in the UBA1 gene, is the focus of this paper. The overview encompasses its discovery, epidemiological traits, genetic underpinnings, and clinical presentations. Delving into whether distinct genotypes yield varied clinical phenotypes in VEXAS patients, and the consequent adjustment of treatment strategies based on genotypic and clinical profiles necessitates thorough exploration within the clinical realm. Additionally, the current therapeutic landscape and future outlook are examined, with particular attention to the potential therapeutic roles of IL-6 inhibitors and JAK inhibitors, alongside an elucidation of prevailing limitations and avenues for further research. This study contributes essential theoretical groundwork and clinical insights for both diagnosing and managing VEXAS syndrome.

Prototype-Guided Graph Reasoning Network for Few-Shot Medical Image Segmentation.

Few-shot semantic segmentation (FSS) is of tremendous potential for data-scarce scenarios, particularly in medical segmentation tasks with merely a few labeled data. Most of the existing FSS methods typically distinguish query objects with the guidance of support prototypes. However, the variances in appearance and scale between support and query objects from the same anatomical class are often exceedingly considerable in practical clinical scenarios, thus resulting in undesirable query segmentation masks. To tackle the aforementioned challenge, we propose a novel prototype-guided graph reasoning network (PGRNet) to explicitly explore potential contextual relationships in structured query images. Specifically, a prototype-guided graph reasoning module is proposed to perform information interaction on the query graph under the guidance of support prototypes to fully exploit the structural properties of query images to overcome intra-class variances. Moreover, instead of fixed support prototypes, a dynamic prototype generation mechanism is devised to yield a collection of dynamic support prototypes by mining rich contextual information from support images to further boost the efficiency of information interaction between support and query branches. Equipped with the proposed two components, PGRNet can learn abundant contextual representations for query images and is therefore more resilient to object variations. We validate our method on three publicly available medical segmentation datasets, namely CHAOS-T2, MS-CMRSeg, and Synapse. Experiments indicate that the proposed PGRNet outperforms previous FSS methods by a considerable margin and establishes a new state-of-the-art performance.

A Gene Cluster of Mitochondrial Complexes Contributes to the Cognitive Decline of COVID-19 Infection.

"Brain fog," a persistent cognitive impairment syndrome, stands out as a significant neurological aftermath of coronavirus disease 2019 (COVID-19). Yet, the underlying mechanisms by which COVID-19 induces cognitive deficits remain elusive. In our study, we observed an upregulation in the expression of genes linked to the inflammatory response and oxidative stress, whereas genes associated with cognitive function were downregulated in the brains of patients infected with COVID-19. Through single-nucleus RNA sequencing (snRNA-seq) analysis, we found that COVID-19 infection triggers the immune responses in microglia and astrocytes and exacerbates oxidative stress in oligodendrocytes, oligodendrocyte progenitors (OPCs), and neurons. Further investigations revealed that COVID-19 infection elevates LUC7L2 expression, which inhibits mitochondrial oxidative phosphorylation (OXPHOS) and suppresses the expression of mitochondrial complex genes such as MT-ND1, MT-ND2, MT-ND3, MT-ND4L, MT-CYB, MT-CO3, and MT-ATP6. A holistic approach to protect mitochondrial complex function, rather than targeting a single molecular, should be an effective therapeutic strategy to prevent and treat the long-term consequences of "long COVID."

Primary vaginal malignant melanoma: A case report.

Malignant melanoma is a rare malignant tumor that can occur in many parts of the body. Primary vaginal malignant melanoma (PVMM) in women accounts for only 3%-7% of all malignant melanomas. PVMM is extremely rare, aggressive, and has a poor prognosis. We report a case of primary vaginal malignant melanoma in order to improve our understanding of the disease.

Clinical characteristics and outcome analysis of pulmonary nocardiosis in southern China: a two-center retrospective study.

BACKGROUND: Pulmonary nocardiosis (PN) is a rare and opportunistic infection. This study aimed to analyze clinical, radiological, and microbiological features, treatment and outcome of PN in southern china. METHODS: Clinical, laboratory, imaging, treatment and outcome data of PN patients at two tertiary hospitals from January 1, 2018, to January 1, 2024 were collected. Factors associated with clinical outcomes were determined by multivariate logistic regression analysis. RESULTS: 67 PN patients including 53 with clinical improvement and 14 with treatment failure were enrolled. Bronchiectasis was the most common respiratory disease in patients with PN (31.3%). The major symptoms of PN were cough (89.6%) and sputum (79.1%). Lung nodules, bronchiectasis, consolidation, pleural involvement, mass, cavity, and lymph node enlargement were the frequent computed tomography findings of PN. Among the Nocardia species detected, N. farcinica was the most common pathogen. Neutrophil-to-lymphocyte ratio (OR = 1.052, p = 0.010), concurrent bacterial infection (OR = 7.706, p = 0.016), and the use of carbapenems (OR = 9.345, p = 0.023) were independently associated with poor prognosis in patients with PN. CONCLUSIONS: This study provides important insights into the clinical features of PN in southern china. neutrophil-to-lymphocyte ratio, concurrent bacterial infection, and the use of carbapenems were independently associated with poor prognosis in patients with PN.

Boundary-Aware Prototype in Semi-Supervised Medical Image Segmentation.

The true label plays an important role in semi-supervised medical image segmentation (SSMIS) because it can provide the most accurate supervision information when the label is limited. The popular SSMIS method trains labeled and unlabeled data separately, and the unlabeled data cannot be directly supervised by the true label. This limits the contribution of labels to model training. Is there an interactive mechanism that can break the separation between two types of data training to maximize the utilization of true labels? Inspired by this, we propose a novel consistency learning framework based on the non-parametric distance metric of boundary-aware prototypes to alleviate this problem. This method combines CNN-based linear classification and nearest neighbor-based non-parametric classification into one framework, encouraging the two segmentation paradigms to have similar predictions for the same input. More importantly, the prototype can be clustered from both labeled and unlabeled data features so that it can be seen as a bridge for interactive training between labeled and unlabeled data. When the prototype-based prediction is supervised by the true label, the supervisory signal can simultaneously affect the feature extraction process of both data. In addition, boundary-aware prototypes can explicitly model the differences in boundaries and centers of adjacent categories, so pixel-prototype contrastive learning is introduced to further improve the discriminability of features and make them more suitable for non-parametric distance measurement. Experiments show that although our method uses a modified lightweight UNet as the backbone, it outperforms the comparison method using a 3D VNet with more parameters.

High-Entropy Materials for Application: Electricity, Magnetism, and Optics.

High-entropy materials (HEMs) have recently emerged as a prominent research focus in materials science, gaining considerable attention because of their complex composition and exceptional properties. These materials typically comprise five or more elements mixed approximately in equal atomic ratios. The resultant high-entropy effects, lattice distortions, slow diffusion, and cocktail effects contribute to their unique physical, chemical, and optical properties. This study reviews the electrical, magnetic, and optical properties of HEMs and explores their potential applications. Additionally, it discusses the theoretical calculation methods and preparation techniques for HEMs, thereby offering insights and prospects for their future development.

Boosting Pseudo-Labeling With Curriculum Self-Reflection for Attributed Graph Clustering.

Attributed graph clustering is an unsupervised learning task that aims to partition various nodes of a graph into distinct groups. Existing approaches focus on devising diverse pretext tasks to obtain suitable supervised information for representation learning, among which the predictive methods show great potential. However, these methods 1) generate auxiliary task bias toward the clustering target and 2) introduce label noise due to static thresholds. To address this issue, we propose a new self-supervised learning method, namely, pseudo-labeling with curriculum self-reflection (PLCSR), that learns reliable pseudo-labels by mining its information to achieve progressive processing of nodes in a self-reflection manner. First, a self-auxiliary encoder is constructed using the exponential moving average (EMA) of the original encoder's parameters to replace the auxiliary tasks, which provides an additional perspective of finding highly confident pseudo-labels. Second, a curriculum selection strategy using dynamic thresholds is designed to take full advantage of graph nodes more accurately. Besides simple nodes with high confidence at the initial stage, nodes that yield consistent predictions from both encoders are then assigned pseudo-labels to avoid the under-learning problem. For the rest difficult nodes that are highly uncertain, we abstain from making judgments to minimize their adverse impact on the model. Extensive experiments have shown that PLCSR significantly outperforms the state-of-the-art predictive method CDRS, achieving more than 6% improvements in terms of clustering accuracy. The code is available at: https://github.com/Jillian555/PLCSR.

Trends in 5-year cancer survival disparities by race and ethnicity in the US between 2002-2006 and 2015-2019.

Racial and ethnic disparities persist in cancer survival rates across the United States, despite overall improvements. This comprehensive analysis examines trends in 5-year relative survival rates from 2002-2006 to 2015-2019 for major cancer types, elucidating differences among racial/ethnic groups to guide equitable healthcare strategies. Data from the SEER Program spanning 2000-2020 were analyzed, focusing on breast, colorectal, prostate, lung, pancreatic cancers, non-Hodgkin lymphoma, acute leukemia, and multiple myeloma. Age-standardized relative survival rates were calculated to assess racial (White, Black, American Indian/Alaska Native, Asian/Pacific Islander) and ethnic (Hispanic, Non-Hispanic) disparities, utilizing period analysis for recent estimates and excluding cases identified solely through autopsy or death certificates. While significant survival improvements were observed for most cancers, notable disparities persisted. Non-Hispanic Blacks exhibited the largest gain in breast cancer survival, with an increase of 5.2% points (from 77.6 to 82.8%); however, the survival rate remained lower than that of Non-Hispanic Whites (92.1%). Colorectal cancer survival declined overall (64.7-64.1%), marked by a 6.2% point drop for Non-Hispanic American Indian/Alaska Natives (66.3-60.1%). Prostate cancer survival declined across all races, with Non-Hispanic American Indian/Alaska Natives showing a decrease of 7.7% points (from 96.9 to 89.2%). Lung cancer, acute leukemia, and multiple myeloma showed notable increases across groups. Substantial racial/ethnic disparities in cancer survival underscore the notable need for tailored strategies ensuring equitable access to advanced treatments, particularly addressing significant trends in colorectal and pancreatic cancers among specific minority groups. Careful interpretation of statistical significance is warranted given the large dataset.