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Genomic characteristics and molecular epidemiology of MRSA from medical centers in Mexico: Results from the Invifar network
Introduction The methicillin-resistant Staphylococcus aureus (MRSA) genome varies by geographical location. This study aims to determine the genomic characteristics of MRSA using whole-genome sequencing (WGS) data from medical centers in Mexico and to explore the associations between antimicrobial resistance genes and virulence factors. Methods This study included 27 clinical isolates collected from sterile sites at eight centers in Mexico in 2022 and 2023. Antibiotic susceptibility testing was performed using VITEK 2. In addition, WGS was performed using a NovaSeq platform, and a bioinformatic analysis was conducted using several tools. Results In this study, 21 strains were CC5, five were CC8, and one was CC93. Moreover, six strains were identified as ST5(CC5)-MRSA-IIa- t895, four strains were found to be ST1011(CC5)-MRSA-IIa-t895, five strains were found to be ST1011(CC5)-MRSA-IIa-t9364, one strain was found to be ST1011(CC5)-MRSA-IIa-t8116, another was found to be ST1011(CC5)-MRSA-IIa-t62, three were found to be ST8(CC8)-MRSA-IVa-t8, one strain was ST5(CC5)-MRSA-IVa-t2, one strain was as ST93(CC93)-MRSA-IVa-t3949, two strains were ST9003(CC8)-MRSA-IVa-t18492, and three strains were ST9034(CC5)-MRSA-V-t2. All SCCmec IIa strains showed resistance to levofloxacin and ciprofloxacin, and all but two strains were resistant to clindamycin. Among the strains that harbored the type IIa cassette, most had the aadD, blaZ, and ermA_SDS genes and the erm A gene. Multiple genes for adhesion, enzymes, immune evasion, and secretion system were detected, regardless of SCCmec type. Of the SCCmec IVa strains, most harbored the Panton-Valentine leukocidin encoding genes. Conclusion In this study, the most frequently detected CC was CC5, followed by CC8, and CC93, and the most frequently detected MRSA ST was ST1011, followed by ST5. Most SCCmec elements were found to be type IIa, followed by type IVa. High MIC values were observed for ciprofloxacin, erythromycin, and clindamycin, particularly within SCCmec IIa. Of the SCCmec IVa strains, most harbored the lukS-PV and lukF-PV genes.
Using machine learning to forecast peak health care service demand in real-time during the 2022–23 winter season: A pilot in England, UK
During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022–23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022–23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems.
Water-regulated viscosity-plasticity phase transitions in a peptide self-assembled muscle-like hydrogel
Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics
Nonlinearity of the post-spinel transition and its expression in slabs and plumes worldwide
Abstract Phase transitions in the mantle control its internal dynamics and structure. The post-spinel transition marks the upper–lower mantle boundary, where ringwoodite dissociates into bridgmanite plus ferropericlase, and its Clapeyron slope regulates mantle flow across it. This interaction has previously been assumed to have no lateral spatial variations, based on the assumption of a linear post-spinel boundary in pressure and temperature. Here we present laser-heated diamond anvil cell experiments with synchrotron X-ray diffraction to better constrain this boundary, especially at higher temperatures. Combining our data with results from the literature, and using a global analysis based on machine learning, we find a pronounced nonlinearity in the post-spinel boundary, with its slope ranging from –4 MPa/K at 2100 K, to –2 MPa/K at 1950 K, and to 0 MPa/K at 1600 K. Changes in temperature over time and space can therefore cause the post-spinel transition to have variable effects on mantle convection and the movement of subducting slabs and upwelling plumes.
Tubulin tyrosination/detyrosination regulate the affinity and sorting of intraflagellar transport trains on axonemal microtubule doublets
Abstract Cilia assembly and function rely on the bidirectional transport of components between the cell body and ciliary tip via Intraflagellar Transport (IFT) trains. Anterograde and retrograde IFT trains travel along the B- and A-tubules of microtubule doublets, respectively, ensuring smooth traffic flow. However, the mechanism underlying this segregation remains unclear. Here, we test whether tubulin detyrosination (enriched on B-tubules) and tyrosination (enriched on A-tubules) have a role in IFT logistics. We report that knockout of tubulin detyrosinase VashL in Chlamydomonas reinhardtii causes frequent IFT train stoppages and impaired ciliary growth. By reconstituting IFT train motility on de-membranated axonemes and synthetic microtubules, we show that anterograde and retrograde trains preferentially associate with detyrosinated and tyrosinated microtubules, respectively. We propose that tubulin tyrosination/detyrosination is crucial for spatial segregation and collision-free IFT train motion, highlighting the significance of the tubulin code in ciliary transport.
Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics
Abstract Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography−mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.
Modulation of Co spin state at Co3O4 crystalline-amorphous interfaces for CO oxidation and N2O decomposition
A principle-based framework to determine countries’ fair warming contributions to the Paris Agreement
Abstract Equity is a cornerstone of global climate policy, yet differing perspectives mean that international agreement on how to allocate mitigation efforts remains elusive. A rich literature informs this question, but a gap remains in approaches that appropriately consider non-CO2 emissions and their warming contributions. In this study, we address this gap and define a global warming budget applicable to all anthropogenic greenhouse gases that is allocated to countries based on principles drawn from international treaties and environmental law. We find that by 2021 a range of 84 to 90 countries, including but not limited to all major developed countries, exhausted their budget share compatible with keeping warming to 1.5 °C (with 50% likelihood) under all allocation approaches considered in this study. A similar picture emerges for limiting warming to 2 °C (with 67% likelihood). A large group of countries will hence exceed their fair shares even if their pledges under the Paris Agreement represent their deepest possible emission reductions. Considerations of fairness should therefore start exploring aspects beyond domestic emissions reductions.
Rescue RM/CS-AKI by blocking strategy with one-dose anti-myoglobin RabMAb
Solvent-dripping modulated 3D/2D heterostructures for high-performance perovskite solar cells
Abstract The controlled growth of two-dimensional (2D) perovskite atop three-dimensional (3D) perovskite films reduces interfacial recombination and impedes ion migration, thus improving the performance and stability of perovskite solar cells (PSCs). Unfortunately, the random orientation of the spontaneously formed 2D phase atop the pre-deposited 3D perovskite film can deteriorate charge extraction owing to energetic disorder, limiting the maximum attainable efficiency and long-term stability of the PSCs. Here, we introduce a meta-amidinopyridine ligand and the solvent post-dripping step to generate a highly ordered 2D perovskite phase on the surface of a 3D perovskite film. The reconstructed 2D/3D perovskite interface exhibits reduced energetic disorder and yields cells with improved performance compared with control 2D/3D samples. PSCs fabricated with the meta-amidinopyridine-induced phase-pure 2D perovskite passivation show a maximum power conversion efficiency of 26.05% (a certified value of 25.44%). Under damp heat and outdoor tests, the encapsulated PSCs maintain 82% and 75% of their initial PCE after 1000 h and 840 h, respectively, demonstrating improved practical durability.
Impacts of irrigation expansion on moist-heat stress based on IRRMIP results
Intratumor heterogeneity of HPV integration in HPV-associated head and neck cancer
Genome-wide profiling of tRNA modifications by Induro-tRNAseq reveals coordinated changes
Longitudinal omics data and preclinical treatment suggest the proteasome inhibitor carfilzomib as therapy for ibrutinib-resistant CLL
Abstract Chronic lymphocytic leukemia is a malignant lymphoproliferative disorder for which primary or acquired drug resistance represents a major challenge. To investigate the underlying molecular mechanisms, we generate a mouse model of ibrutinib resistance, in which, after initial treatment response, relapse under therapy occurrs with an aggressive outgrowth of malignant cells, resembling observations in patients. A comparative analysis of exome, transcriptome and proteome of sorted leukemic murine cells during treatment and after relapse suggests alterations in the proteasome activity as a driver of ibrutinib resistance. Preclinical treatment with the irreversible proteasome inhibitor carfilzomib administered upon ibrutinib resistance prolongs survival of mice. Longitudinal proteomic analysis of ibrutinib-resistant patients identifies deregulation in protein post-translational modifications. Additionally, cells from ibrutinib-resistant patients effectively respond to several proteasome inhibitors in co-culture assays. Altogether, our results from orthogonal omics approaches identify proteasome inhibition as potentially attractive treatment for chronic lymphocytic leukemia patients resistant or refractory to ibrutinib.
Temperature seasonality regulates organic carbon burial in lake
Single soliton microcomb combined with optical phased array for parallel FMCW LiDAR
Mechano-gated iontronic piezomemristor for temporal-tactile neuromorphic plasticity
Abstract In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity. This system utilizes a bicontinuous phase-transition heterogel as a stiffness-governed iontronic mechanogate to achieve bidirectional piezoresistive signals, resulting in wide-span dynamic tactile sensing. By micro-integrating the mechanogate with an oscillatory iontronic memristor, it exhibits stiffness-induced bipolarized excitatory and inhibitory neuromorphics, thereby enabling the activation of temporal-tactile memory and learning functions (e.g., Bienenstock–Cooper–Munro and Hebbian learning rules). Owing to dynamic covalent bond network and iontronic features, reconfigurable tactile plasticity can be achieved. Importantly, bridging to bioneuronal interfaces, these systems possess the capacity to construct a biohybrid perception-actuation circuit. We anticipate that such temporal plastic piezomemristor devices for abiotic-biotic interfaces can serve as promising hardware systems for interfacing dynamic tactile behaviors into diverse neuromodulations.
Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes
Abstract Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophosphates are among the most promising solid electrolytes, as they exhibit superionic conductivity at room temperature. However, the lack of comprehensive understanding of their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations in all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. The results show that disorder-driven diffusion dynamics significantly enhances the room-temperature conductivity. We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed “softness”. This metric allows the classification of the disorder-induced “soft” hopping lithium ions. Our findings offer insights into ion conduction mechanisms in complex disordered structures, thereby contributing to the development of superior solid-state electrolytes for LIBs.