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LDL receptor promotes urinary tract infection by α-hemolysin-producing <i>Escherichia coli</i> and long-term histopathologic sequelae in the kidney
Sentiment analysis of classical Chinese literature: An unsupervised deep learning model with BERT and graph attention networks
Sentiment analysis has become a transformative technology in various contexts, particularly in Natural Language Processing (NLP), social media analytics, and literary analysis, as it can extract information from a wide range of texts. The advancements in deep learning, particularly with transformer models such as BERT and graph-based models like GATs, have enabled faster progress in analyzing complex language structures. However, the issue lies in incorporating these technologies into classical Chinese literature, which involves delicate syntax, semantics, and emotions that are difficult to harness using traditional methods. The existing methods, which rely on strictly labeled data or unsupervised learning methods that do not effectively manage contextual dependencies, are very limited in analyzing historical or philosophical texts that abound in metaphor and implicit sentiment. To minimize the limitations, this paper proposes an unsupervised deep learning framework that integrates BERT embeddings, sentiment lexicon enrichment, and graph attention networks (GATs) for sentiment analysis in classical Chinese literature. Firstly, the BERT-based model extracts contextualised embeddings from a raw text, providing a deep understanding of semantics. Secondly, embedding includes sentiment-specific data from the NTUSD lexicon, thus injecting it with emotional information. Thirdly, a graph-based formulation is developed, in which words are represented as nodes, and the relations between them are defined using GATs to modify the features of nodes based on their significance in the context. Finally, unsupervised sentiment labelling, or K-Means clustering, is used to classify sentiment. The experimental results demonstrate the proposed model’s efficiency – an accuracy of 0.95, precision of 0.97, recall of 0.96, and F1-score of 0.91 in several runs. These results surpass those of the traditional approach, which includes SentiCNN, MLT-ML4, and BERT-LLSTM-DL, which achieve an accuracy score of 0.90 to 0.95. Additionally, the comparison with large-scale foundation models (such as ChatGPT-4o and DeepSeek R1) in zero-shot prompt-based classification further validates the domain-adapted advantage of our model in the classical Chinese text processing. These results demonstrate that the proposed model significantly enhances the handling of the intricate linguistic features and cultural nuances in classical Chinese texts, providing a robust solution for sentiment analysis in low-resource domains.
Wnt inhibition alleviates resistance to anti-PD1 therapy and improves antitumor immunity in glioblastoma
Wnt signaling plays a crucial role for many developmental processes. It is also pivotal in the generation and limited treatment outcomes of glioblastoma (GBM). Here, we identified Wnt7b, which is markedly upregulated in GBM patients, as a determinant of resistance to immune checkpoint blockers (αPD1; anti-Programmed Cell Death Protein 1) in a clinically relevant, αPD1-resistant GBM murine model with abundant stem cells. We observed that increased levels of Wnt7b and β-catenin correlated with the resistance to αPD1. Treatment combining a porcupine inhibitor WNT974 with αPD1 reprogrammed the immune suppressive tumor microenvironment (TME) to bolster antitumor immune responses and extended the survival of mice bearing orthotopic GBM, with 25% long-term survivors. Our causal studies revealed that WNT974 potentiated αPD1 therapy by the expansion of antigen presenting DC3-like dendritic cells (DCs). Additionally, WNT974 combination with αPD1 was associated with a reduction in immune suppressive granulocytic myeloid-derived suppressor cells (MDSCs), an increase in the Ki67+CD8/Ki67+regulatory T cells (Treg) ratio, tilting the CD8:Treg balance in the TME toward antitumor immune response, and more pronounced GrzB+CD8+ effector T cells. Conversely, an increase in monocytic MDSCs and phosphorylation of pro-oncogenic proteins was associated with resistance to the combination therapy. Collectively, our preclinical findings provide a strong rationale to test Wnt7b/β-catenin inhibition with αPD1 therapy in GBM patients with elevated Wnt7b/β-catenin signaling.
A solid-state battery capable of 180 C superfast charging and 100% energy retention at –30 °C
Solid-state electrolytes (SSEs) are being extensively researched as replacements for liquid electrolytes in future batteries. Despite significant advancements, there are still challenges in using SSEs, particularly in extreme conditions. This study presents a hydrated metal-organic ionic cocrystal (HMIC) solid-state ion conductor with a solvent-assisted ion transport mechanism suitable for extreme operating conditions. Through crystal engineering strategies, the adsorption capacity of HMIC for anions and water molecules can be regulated, thereby facilitating cation hopping transport and enhancing electrochemical stability. As a result, optimized HMIC shows exceptional properties, including an extraordinarily high Zn 2+ transference number (t Zn2+ = 0.81), an expanded electrochemical stability window (~2.6 V), and an exceptionally high Zn 2+ ion conductivity (8.6 mS cm –1 , 25 °C). Interface dynamics analysis indicates that this strong binding to water molecules can significantly reduce the desolvation energy barrier and enhance the ionic diffusion coefficient. (10 to 100 times higher than that in aqueous electrolytes). This allows Zn|| Prussian blue analog batteries to exhibit impressive fast-charging performance (180 C, 20 s, over 1,000 charge/discharge cycles) and maintain 100% discharge capacity retention and discharge plateau from –30 to 30 °C. The development of HMICs with a solvent-assisted hopping mechanism provides a promising path for solid-state zinc-ion batteries in extreme conditions, including fast charging, low temperature, and high loading.
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A galanin-positive population of lumbar spinal cord neurons modulates sexual arousal and copulatory behavior in male mice
Abstract During sex, male arousal builds to the ejaculatory threshold, allowing genital sensory input to trigger ejaculation. While copulation and arousal are thought to be brain-regulated, ejaculation is a reflex controlled by a spinal circuit. In this framework, the spinal cord is assumed to be strongly inhibited by descending input until the ejaculatory threshold, playing no role in the regulation of copulatory behavior. However, this remains untested. Here we mapped the spinal circuit controlling the bulbospongiosus muscle, essential for sperm expulsion in mice. Our findings show that bulbospongiosus muscle-motor neurons receive input from galanin-expressing neurons, which integrate genital sensory signals. Stimulating these neurons induces bulbospongiosus activity, but responses vary with spinalization, internal state, and decrease with repeated stimulation. Ablating galanin-positive neurons altered ejaculation latency and copulatory patterns. These results suggest that spinal circuits influence not only ejaculation but also copulation and arousal, challenging the traditional view of spinal control in copulation.
Scalable and cost-effective methods for xenomonitoring of P. falciparum and antimalarial drug resistance validated with laboratory and wild-caught mosquitoes
Abstract Human blood samples serve as the gold standard for molecular surveillance of the malaria parasite Plasmodium falciparum. However, these samples may not accurately reflect the parasite population and come with logistical constraints and ethical requirements. Using blood-fed mosquitoes as a sample basis could overcome these challenges. We developed and validated DNA extraction methods and PCR assays, allowing for P. falciparum detection from whole mosquitoes and sequencing of drug resistance genes. PCRs were consistently positive on mosquito samples mimicking field conditions, i.e., with low P. falciparum infection (2000× dilution of a single oocyst-infected mosquito) or after a blood-meal with low parasitemia (250× dilution of 1% parasite density). Antimalarial drug resistance genes PfK13 and PfMDR1 could be sequenced from these samples, and markers of resistance could be detected in samples reflecting a mutated minority parasite population of ≥ 25%. Pools of 20 mosquitoes allowed for the detection of a single infected mosquito. We applied our protocol on 50 field-caught Anopheles mosquitoes from DR Congo. Five out of eight mosquito pools were P. falciparum positive, providing good sequencing reads for PfK13 and PfMDR1. Our presented method has the potential to facilitate surveillance, e.g., on drug resistance, leveraging the promising properties of xenomonitoring.
Trump team backs an unproven drug for autism — but does it work?
Latest Cretaceous megaraptorid theropod dinosaur sheds light on megaraptoran evolution and palaeobiology
Sustainable production of silver chloride nanoparticles from desert flora for biomedical applications with multifunctional biological activities
Abstract In this work, Euphorbia sanctae-catharinae leaf extract is used for the first time to biosynthesize silver chloride nanoparticles (AgCl-NPs) via a green synthesis approach. The formation and properties of the AgCl-NPs were confirmed using various characterization techniques: UV–Vis spectroscopy showed a characteristic absorption peak at 430 nm, indicating nanoparticle formation; X-ray diffraction (XRD) analysis revealed a crystallite size of approximately 24 nm; transmission electron microscopy (TEM) showed predominantly spherical nanoparticles with sizes ranging from 20 to 50 nm; and Fourier-transform infrared spectroscopy (FTIR) identified functional groups from the plant extract involved in nanoparticle stabilization. The MIC values were 62.5 µg/mL for (S. aureus, E. coli, E. faecalis, S. faecalis, E. aerogenes), 250 µg/mL for (S. epidermidis, K. pneumoniae, R. ornithinolytica), and 125 µg/mL for P. aeruginosa. The MBC values were 500 µg/mL for (R. ornithinolytica, S. faecalis, K. pneumoniae, S. epidermidis), and 250 µg/mL for (S. aureus, E. coli, E. faecalis), and 125 µg/mL for E. aerogenes. Tetracycline’s synergistic actions increased antibacterial effectiveness by 21.4–47%. Furthermore, at a non-toxic dose (MNTC: 31.25 µg/mL), AgCl-NPs showed strong antiviral activity against HSV-1, preventing viral multiplication by 74%. These results demonstrate the potential of AgCl-NPs produced from Euphorbia sanctae-catharinae as a sustainable substitute for fighting viral infections and antibiotic resistance.
Journals infiltrated with ‘copycat’ papers that can be written by AI
Clinical implications of bone marrow adiposity identified by phenome-wide association and Mendelian randomization in the UK Biobank
Abstract Bone marrow adiposity changes in diverse diseases, but the full scope of these, and whether they are directly influenced by marrow adiposity, remains unknown. To address this, we previously measured the bone marrow fat fraction of the femoral head, total hip, femoral diaphysis, and spine of over 48,000 UK Biobank participants. Here, we first use these data for PheWAS to identify diseases associated with marrow adiposity at each site. This reveals associations with 47 incident diseases across 12 disease categories, including osteoporosis, fracture, type 2 diabetes, cardiovascular diseases, cancers, and other conditions that burden public health worldwide. Intriguingly, type 2 diabetes associates positively with spine bone marrow adiposity but negatively with marrow adiposity at femoral sites. We then establish PRSs based on bone-marrow-fat-fraction-associated SNPs and use PRS-PheWAS and Mendelian randomization to explore causal associations between marrow adiposity and disease. PRS-PheWAS reveals that genetic predisposition to increased marrow adiposity is positively associated with osteoporosis and fractures. Mendelian randomization further suggests that increased marrow adiposity at the diaphysis and total hip is causally associated with osteoporosis. Our findings substantially advance understanding of how marrow adiposity impacts human health and highlight its potential as a biomarker and/or therapeutic target for diverse human diseases.
Multi-label machine learning for power forecasting of a grid-connected photovoltaic solar plant over multiple time horizons
Abstract Because of solar power’s inherent intermittency and stochastic nature, accurate photovoltaic (PV) generation forecasting is critical for the planning and operation of PV-integrated power systems. Thus, accurate power forecasting becomes vital for maintaining good power dispatch efficiency and power grid operational security. Several PV forecasting methods based on machine learning algorithms (MLAs) have recently emerged. This paper presents machine learning methods for multi-label forecasting of PV and AC power delivered to the grid of a building-applied PV plant. Various algorithms representing multiple groups are evaluated, including linear regression (LR), polynomial regression (PR), neural networks (NN), deep learning (DL), gradient-boosted trees (GBT), random forests (RF), decision trees (DT), k-nearest neighbor (k-NN), and support vector machines (SVM). The models use real-time collected data from sensors over one year for solar irradiance, ambient temperature, wind speed, and cell temperature to predict PV and AC power outputs. Forecast performance over multiple time horizons is validated using four datasets: 24 h, one week, one month, and sudden variations. Models are evaluated based on performance metrics such as absolute error (AE), root mean square error (RMSE), normalized absolute error (NAE), relative error (RE), relative root square error (RRSE), and correlation coefficient (R). Results show that RF, DT, and DL consistently achieved the highest accuracy (R ≈ 99.8–100%) with minimal errors (RMSE within 0.014–0.022, AE within 0.008–0.015) across various forecasting scenarios. These models demonstrated strong adaptability and predictive reliability across short-term, medium-term, and long-term forecasts, making them the most effective choices for PV and AC power prediction. The accurate forecasts generated in this study have the potential to aid grid operators in forecasting PV power output variability and planning for integrating intermittent PV power into the grid. Understanding how PV generation will fluctuate given different meteorological conditions allows operators to ensure the consistent integration of this weather-dependent power source. Moreover, multi-label prediction of DC and AC power enables inverter efficiency optimization and grid integration analysis. The average actual and predicted efficiencies of the inverter are 0.96688 and 0.9638, providing valuable insights.
Fossil fight: how Raymond Dart countered some unfair criticism
PBK/TOPK mediates Ikaros, Aiolos and CTCF displacement from mitotic chromosomes and alters chromatin accessibility at selected C2H2-zinc finger protein binding sites
Abstract PBK/TOPK is a mitotic kinase implicated in haematological and non-haematological cancers. Here we show that the key haemopoietic regulators Ikaros and Aiolos require PBK-mediated phosphorylation to dissociate from chromosomes in mitosis. Eviction of Ikaros is rapidly reversed by addition of the PBK-inhibitor OTS514, revealing dynamic regulation by kinase and phosphatase activities. To identify more PBK targets, we analysed loss of mitotic phosphorylation events in Pbk –/– preB cells and performed proteomic comparisons on isolated mitotic chromosomes. Among a large pool of C2H2-zinc finger targets, PBK is essential for evicting the CCCTC-binding protein CTCF and zinc finger proteins encoded by Ikzf1, Ikzf3, Znf131 and Zbtb11. PBK-deficient cells were able to divide but showed altered chromatin accessibility and nucleosome positioning consistent with CTCF retention. Our studies reveal that PBK controls the dissociation of selected factors from condensing mitotic chromosomes and contributes to their compaction.