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IL-1 protects from fatal systemic candidiasis in mice by inhibiting oxidative phosphorylation and hypoxia
Abstract Invasive C. albicans infections result in high mortality rates. While IL-1 is important to combat C. albicans infections, the underlying mechanisms remain unclear. Using global and conditional Il1r1 knockouts in mice, here we show that IL-1R signaling in non-hematopoietic cells in the kidney and brain is crucial for a protective response. In the kidney, endothelial IL-1R contributes to fungal clearance independent of neutrophil recruitment, while IL-1R in hematopoietic cells is dispensable. IL-1R signaling indirectly recruits neutrophils and monocytes in the brain by regulating chemokines and adhesion molecules. Single-nucleus-RNA-sequencing data implicates excessive metabolic activity and oxidative phosphorylation across all cell types in the kidney of Il1r1-deficient mice within a few hours upon infection, with associated, localized hypoxia at infection foci. Lastly, we find that hypoxia promotes fungal growth and pathogenicity. In summary, our results show that IL-1R-signaling in non-hematopoietic cells is required to prevent fatal candidiasis by inhibiting a metabolic shift, including excessive oxidative phosphorylation and hypoxia.
Multi-omic analysis of SDHB-deficient pheochromocytomas and paragangliomas identifies metastasis and treatment-related molecular profiles
Abstract Hereditary SDHB -mutant pheochromocytomas (PC) and paragangliomas (PG) are rare tumours with a high propensity to metastasize although their clinical behaviour is unpredictable. To characterize the genomic landscape of these tumours and identify metastasis biomarkers, we perform multi-omic analysis on 94 tumours from 79 patients using seven molecular methods. Sympathetic (chromaffin cell) and parasympathetic (non-chromaffin cell) PCPG have distinct molecular profiles reflecting their cell-of-origin and biochemical profile. TERT and ATRX -alterations are associated with metastatic PCPG and these tumours have an increased mutation load, and distinct transcriptional and telomeric features. Most PCPG have quiet genomes with some rare co-operative driver events, including EPAS1 /HIF-2α mutations. Two mechanisms of acquired resistance to DNA alkylating chemotherapies are identifiable; MGMT overexpression and mismatch repair-deficiency causing hypermutation. Our comprehensive multi-omic analysis of SDHB -mutant PCPG therefore identifies features of metastatic disease and treatment response, expanding our understanding of these rare neuroendocrine tumours.
Observation of the spiral spin liquid in a triangular-lattice material
Abstract The spiral spin liquid (SSL) is a highly degenerate state characterized by a continuous contour or surface in reciprocal space spanned by a spiral propagation vector. Although the SSL state has been predicted in a number of various theoretical models, very few materials are so far experimentally identified to host such a state. Via combined single-crystal wide-angle and small-angle neutron scattering, we report observation of the SSL in the quasi-two-dimensional delafossite-like AgCrSe2. We show that it is a very close realization of the ideal Heisenberg J 1–J 2–J 3 frustrated model on the triangular lattice. By supplementing our experimental results with microscopic spin-dynamics simulations, we demonstrate how such exotic magnetic states are driven by thermal fluctuations and exchange frustration.
Observational constraints on the strength and depth of the zonal jets on Saturn
The nuclear exosome co-factor MTR4 shapes the transcriptome for meiotic initiation
Microscopic origin of abrupt mixed-order phase transitions
Endocrine-exocrine miR-503-322 drives aging-associated pancreatitis via targeting MKNK1 in acinar cells
Manipulations of a transmon qubit with a null-biased electro-optic fiber link
A supramolecular bactericidal material for preventing and treating plant-associated biofilms
Entanglement Rényi negativity of interacting fermions from quantum Monte Carlo simulations
Abstract Many-body entanglement unveils additional aspects of quantum matter and offers insights into strongly correlated physics. While ground-state entanglement has received much attention in the past decade, the study of mixed-state quantum entanglement using negativity in interacting fermionic systems remains largely unexplored. We demonstrate that the partially transposed density matrix of interacting fermions, similar to their reduced density matrix, can be expressed as a weighted sum of Gaussian states describing free fermions, enabling the calculation of rank-n Rényi negativity within the determinant quantum Monte Carlo framework. We calculate the rank-two Rényi negativity for the half-filled Hubbard model and the spinless t-V model. Our calculation reveals that the area law coefficient of the Rényi negativity for the spinless t-V model has a logarithmic finite-size scaling at the finite-temperature transition point. Our work contributes to the calculation of entanglement and sets the stage for future studies on quantum entanglement in various fermionic many-body mixed states.
Deep indel mutagenesis reveals the impact of amino acid insertions and deletions on protein stability and function
Thermomagnetic liquid metal switches with fast bidirectional response
Comparative genomics provides insights into the biogeographic and biochemical diversity of meliaceous species
Governance and responses of health and surveillance systems to COVID-19 in BRICS countries: A scoping review protocol
The capacity of countries to respond to COVID-19 has varied, worsening inequities. Structural factors (such as socio-economic inequalities and health system financing conditions) and political-institutional factors (such as the leadership and directionality of government action) have a major influence on the structure and outcomes of national responses. An effective COVID-19 response requires strong national governance, coordination strategies, and integration between health system actions and public health surveillance measures. This scoping review aims to synthesize the evidence on the governance and responses of the BRICS countries to the COVID-19 pandemic within a context of structural inequalities, identifying lessons and gaps in the current literature. This protocol was guided by the scoping review methodology, developed by the Joanna Briggs Institute, and used the PRISMA-P reporting guidelines. Searches will be carried out on the BVS Portal, Pubmed, Web of Science, Scopus, Embase and Dimensions databases. Inclusion criteria include secondary sources in the form of scientific publications, complemented by government and institutional documents published between 2020 and 2024. Exclusion criteria exclude publications that do not involve the selected countries, focus only on local or regional level without contemplating national-level elements, and/or address with COVID-19 solely from a clinical perspective. The scoping review process will select and organize retrieved data using the Rayyan software. Six independent reviewers will select the articles, working in three pairs. The results will be described, analyzed, and categorized through a descriptive synthesis, correlating them with the research objectives and questions. Registration This protocol is registered on the Open Science Framework (OSF) (available at https://doi.org/10.17605/OSF.IO/SN5ZY) and Figshare (available at https://doi.org/10.6084/m9.figshare.25908340) platforms.
New adjusted missing value imputation in multiple regression with simple random sampling and rank set sampling methods
This research compared the efficiency of several adjusted missing value imputation methods in multiple regression analysis. The four imputation methods were the following: regression-ratio quartile1,3 (R-RQ1,3) imputation of Al-Omari, Jemain and Ibrahim; adjusted regression-chain ratio quartile1,3 (AR-CRQ1,3) imputation of Kadilar and Cinji; adjusted regression-multivariate ratio quatile1,3 (AR-MRQ1,3) imputation of Feng, Ni, and Zou; and adjusted regression-multivariate chain ratio quartile1,3 (AR-MCRQ1,3) imputation of Lu for each simple random sampling (SRS) and rank set sampling (RSS). The performance measures mean square error (MSE) and mean absolute percentage error (MAPE). The study showed that the AR-MRQ1 method with SRS provided the minimum mean square error for small error variance. However, the AR-MCRQ3 provided the minimum mean square error for a large error variance. Considering all error variance in mean absolute percentage error, the AR-MCRQ1 provided the minimum mean absolute percentage error. The AR-MRQ1 method with RSS provided the minimum mean square error for a small error variance. However, the AR-MCRQ3 provided the minimum mean square error for medium and large error variance. Regarding the mean absolute percentage error measure, the AR-MRQ1 provided the minimum mean absolute percentage error for a small error variance. However, the AR-MCRQ1 provided the minimum mean absolute percentage error for medium and large error variance. For both SRS and RSS, AR-MCRQ1 was the best method for missing value imputation in multiple regression analysis, followed by AR-MCRQ3. Moreover, the RSS estimators provided smaller MSE and MAPE than the SRS estimators. Therefore, the RSS estimators were more efficient than the SRS estimators.
Demographic, socioeconomic and regional disparities in the coverage of water, sanitation and hygiene facilities in four South Asian Countries
Background Ensuring an adequate water, sanitation, and hygiene (WASH) is crucial for upholding public health and achieving Sustainable Development Goals (SDG-6). The main goal of this study was to review the existing WASH facilities, mapping for regional comparisons, and identify the significant socioeconomic and demographic factors associated with WASH facilities in Afghanistan, Bangladesh, Nepal, and Pakistan. Methods This study employed a quantitative research design using the most recent Multiple Indicator Cluster Survey (MICS) data from Afghanistan (2022–23, n = 23,213), Bangladesh (2019, n = 61,242), Nepal (2019, n = 12,655), and Pakistan (2017–19, n = 96,105). Data analysis was done using descriptive statistics and multivariate logistic regression model. Besides, spatial mapping was used for regional comparison, the Generalized Variance Inflation Factor (GVIF) was applied for checking multicollinearity, and the Receiver Operating Characteristic (ROC) curve was used to evaluate model performance. Results This study revealed substantial disparities in the coverage of WASH facilities across four countries. Nepal had the highest coverage (75.33%), followed by Pakistan (59.47%), Bangladesh (50.28%) and Afghanistan (33.54%). Wealthier households were associated with higher odds of WASH facilities compared to the poor: Afghanistan (aOR = 7.83; 95% CI: 6.58–9.32; p < 0.001), Bangladesh (aOR = 5.75; 95% CI: 5.34–6.20; p < 0.001), Nepal (aOR = 5.80; 95% CI: 4.52–7.44; p < 0.001), and Pakistan (aOR = 9.64; 95% CI: 8.79-10.58; p < 0.001). In addition, place of residence, education of household head, access to the media, age of household head, and family size emerged as significant determinants of WASH facilities across all four countries. Conclusion The findings suggest that wealthier households, those with educated heads, and those in urban areas have higher coverage of WASH facilities. To ensure adequate WASH facilities, policymakers should focus on rural areas, lower-income groups, less educated household heads, and should conduct awareness campaigns.
Classification of pulmonary diseases from chest radiographs using deep transfer learning
Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Chest radiographs are commonly used to diagnose pulmonary diseases. In clinical practice, diagnosing pulmonary diseases using chest radiographs is challenging due to Overlapping and complex anatomical Structures, variability in radiographs, and their quality. The availability of a medical specialist with extensive professional experience is profoundly required. With the use of Convolutional Neural Networks in the medical field, diagnosis can be improved by automatically detecting and classifying these diseases. This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. Our proposed deep transfer learning-based computational model achieved promising results as compared to existing state-of-the-art methods. Our model reported an overall specificity of 97.92%, a sensitivity of 97.30%, a precision of 97.94%, and an Area under the Curve of 97.61%. It has been observed that the promising results of our proposed model will be valuable tool for practitioners in decision-making and efficiently diagnosing various pulmonary diseases.
STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network
As the financial market becomes increasingly complex, stock prediction and anomaly data detection have emerged as crucial tasks in financial risk management. However, existing methods exhibit significant limitations in handling the intricate relationships between stocks and addressing anomalous data. This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. Experimental results show that the complete STAGE framework achieved an accuracy of 85% after 20 training epochs, which is 10% to 20% higher than models with key algorithms removed. In the anomaly detection task, the STAGE framework further improved the accuracy to 95%, demonstrating fast convergence and stability. This framework offers an innovative solution for stock prediction, adapting to the complex dynamics of real-world markets.