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Isolation and characterization of 24 phages infecting the plant growth-promoting rhizobacterium Klebsiella sp. M5al

Bacteriophages largely impact bacterial communities via lysis, gene transfer, and metabolic reprogramming and thus are increasingly thought to alter nutrient and energy cycling across many of Earth’s ecosystems. However, there are few model systems to mechanistically and quantitatively study phage-bacteria interactions, especially in soil systems. Here, we isolated, sequenced, and genomically characterized 24 novel phages infecting Klebsiella sp. M5al, a plant growth-promoting, nonencapsulated rhizosphere-associated bacterium, and compared many of their features against all 565 sequenced, dsDNA Klebsiella phage genomes. Taxonomic analyses revealed that these Klebsiella phages belong to three known phage families (Autographiviridae, Drexlerviridae, and Straboviridae) and two newly proposed phage families (Candidatus Mavericviridae and Ca. Rivulusviridae). At the phage family level, we found that core genes were often phage-centric proteins, such as structural proteins for the phage head and tail and DNA packaging proteins. In contrast, genes involved in transcription, translation, or hypothetical proteins were commonly not shared or flexible genes. Ecologically, we assessed the phages’ ubiquity in recent large-scale metagenomic datasets, which revealed they were not widespread, as well as a possible direct role in reprogramming specific metabolisms during infection by screening their genomes for phage-encoded auxiliary metabolic genes (AMGs). Even though AMGs are common in the environmental literature, only one of our phage families, Straboviridae, contained AMGs, and the types of AMGs were correlated at the genus level. Host range phenotyping revealed the phages had a wide range of infectivity, infecting between 1–14 of our 22 bacterial strain panel that included pathogenic Klebsiella and Raoultella strains. This indicates that not all capsule-independent Klebsiella phages have broad host ranges. Together, these isolates, with corresponding genome, AMG, and host range analyses, help build the Klebsiella model system for studying phage-host interactions of rhizosphere-associated bacteria.

Leuconostoc lactis strain APC 3969 produces a new variant of cyclic bacteriocin leucocyclicin Q and displays potent anti-Clostridium perfringens activity

Adsorption of methanol on cationic cobalt clusters in the gas phase

Knowledge about the adsorption and activation of methanol on metal catalysts is essential to obtain insights into the conversion of methanol to sustainable chemicals. In this work, the adsorption of methanol on Con+ (n = 1–60) clusters is investigated using low-pressure collision cell experiments in combination with time-of-flight mass spectrometry. Experiments are conducted using both methanol and deuterated methanol in order to examine potential isotope effects and to gain insights into the reaction mechanism. Kinetic data and Rice–Ramsperger–Kassel–Marcus calculations indicate the absence of methanol desorption for n < 10 cluster sizes, suggesting dissociative chemisorption of methanol for those sizes. For larger clusters, the reaction involves a combination of association and desorption, with a pronounced size dependence of the corresponding reaction rates. This size dependence is anti-correlated with the promotion energy of an electron from an occupied frontier orbital to the lowest unoccupied d-state.

Controls on concentrations and clumped isotopologues of vehicle exhaust methane

Methane emissions from vehicle exhaust, as a source of methane, are often overlooked. However, in areas with high vehicle activity, the emissions can be substantial. There is a notable lack of characterization regarding the variable concentrations and isotopic signatures of methane in vehicle exhaust. This gap in knowledge limits our understanding of the mechanisms of methane production in vehicles and the factors controlling concentration variations and isotopic fractionation, which also makes it difficult to identify and reduce methane emissions from vehicle exhaust. This study characterized the methane concentration ([CH4]), methane-to-ethane ratio (C2:C1), methane carbon and hydrogen isotopes (δ13C and δD), and methane clumped isotopologues (Δ13CH3D and Δ12CH2D2) of the vehicle exhaust methane endmember. [CH4] varied widely from below 1 ppm to more than 3000 ppm, potentially influenced by vehicle maintenance and operational phases. Ethane concentrations ([C2H6]) correlated with [CH4], yet C2:C1 varied significantly from 0.1% to 18.3%. The δ13C and δD values of exhaust methane were less negative than those of natural gas. A large portion of samples showed a positive linear relationship between [CH4], δ13C from -22‰ to -11‰, and δD values from -170‰ to -120‰, while their clumped isotopologues exhibit ~0.8‰ clumping in Δ13CH3D and ~-2.4‰ anti-clumping in Δ12CH2D2. A small portion of the samples exhibited distinct isotopic characteristics, with their δ13C and δD values either becoming significantly more positive or aligning closer to the composition of ambient air, while their Δ12CH2D2 values showed a marked increase, reaching between +25‰ to +33‰. These concentration and isotope characteristics show trends that can be explained by a combination of processes, including 1) methane formation in the engine, 2) methane combustion in the engine, 3) methane oxidation by the catalytic converter, and 4) mixing with air. The observed isotopic fractionation can be explained by thermo equilibrium and Rayleigh fractionations. These processes, elucidated through isotopic and clumped isotopologue analyses, underscore the intricate dynamics and controls of vehicular methane emissions.

Author Correction: Temporal dynamics of uncertainty and prediction error in musical improvisation across different periods

Liouville-space response theory in the self-consistent field approximation

We present a second-quantization based Liouville-space formulation of response theory for non-eigenstates of unperturbed Hamiltonian in the single-determinant self-consistent field framework, where we include a time-independent relaxation superoperator in the Liouville equation of motion. This density-based formulation uses quantities and concepts similar to those introduced in established wave function-based forms of approximate-state response theory, and we discuss how the wave function-based class of theory relates to the present more general treatment. We also discuss various aspects of the present methodology, including its opportunities and limitations/challenges, and outline future work.

Epidemiology, literacy, risk factors, and clinical status of oral cancer in East Africa: A scoping review

Background Oral cancer (OC) is a topical public health issue in East Africa due to increasing incidence of the disease. Public health efforts to address the oral cancer burden depends largely on the available empirical evidence. Hence, this scoping review aims to map the existing empirical evidence on oral cancer in East African countries. Methods The Preferred Reporting Items for Systematic Review and Meta-analysis Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline for reporting this scoping review. Additionally, we ensured quality assessment of the methodology and reporting process of this study using the AMSTAR 2 checklist. We conducted a systematic search of nine research databases on 17th November 2023, and reviewed studies published in English from year 2000 to 17th November 2023. The team developed data extraction form and data extraction was done by two reviewers. Thematic analyses were conducted manually and presented in texts, tables and flow chart. Results Only 30 full manuscripts were included in this review. Twenty-nine out of 30 studies were either hospital- or clinic-based while two were community-based. Only four studies showed gaps and obvious disparities in awareness and knowledge levels across East Africa, however, higher levels of awareness were reported among dentists and dental patients relative to the general population. Most neoplasms were presented and diagnosed late. The review finding also highlighted the significant impact of Toombak use on the oral microbiome composition, potentially contributing to oral cancer risks. Further, this review elucidated the prognostic relevance of PD-L1 expression at the invasive tumor front and microbial composition, with Candida correlating with adverse prognosis and Malassezia showing associations with improved survival rates. Also, Toombak usage, tumor staging, and mucosal field alterations emerged as predictors of local recurrence, while lymph node involvement and extranodal extension were associated with regional recurrence among Sudanese cohorts. Finally, a few studies undertook an evaluation of instrument validity for OC detection, revealing promising outcomes concerning diagnostic accuracy and instrument reliability. Conclusions There is a dire need for targeted interventions and early detection strategies tailored to the unique epidemiological and clinical profiles of oral and maxillofacial tumors in East Africa. Public health interventions aimed at curbing the prevalence of Toombak use and promoting healthier lifestyle choices to reduce the oral diseases incidence in Sudan and other regions where these behaviors are prevalent remain germane.

Author Correction: Wild Andean camelids promote rapid ecosystem development after glacier retreat

High-energy irradiation of matrix isolated acetic acid yields a ·CH3⋯CO2 complex: A spectroscopic and <i>ab initio</i> study

The mechanism of the chemical transformations of isolated small organic molecules induced by high-energy radiation is of basic interest for astrophysics and astrochemistry. In this work, we first applied a combination of electron paramagnetic resonance (EPR) and Fourier-transform infrared (FTIR) spectroscopy to identify the products of the radiation-induced transformations of isolated CH3COOH and CD3COOH molecules. As revealed by EPR, ·CH3 (or ·CD3) is the principal primary radical generated from acetic acid in solid argon and xenon, while the FTIR results suggest that this radical is trapped mainly in the form of the ·CH3⋯CO2 radical–molecule complex. The assignment of this previously unknown complex was based on the complexation-induced shifts of the absorption bands corresponding to CH3OPLA and CO2bend vibration modes, confirmed by analysis of the kinetic curves, photochemical behavior, and comparison with the results of ab initio computations at the spin-unrestricted coupled-cluster singles, doubles, and perturbative triples level of theory. Most likely, the complex in matrices adopts the geometry close to the theoretically predicted structure with Cs symmetry stabilized by the C⋯C and O⋯H interactions. It was suggested that the complex could be produced via the intermediate formation of a CH3COOH+· radical cation deprotonating to the CH3COO· radical, which promptly decomposed to ·CH3 + CO2 fragments. We believe that the results obtained in this study may contribute to a better understanding of the processing of acetic acid molecules in astrophysically relevant ices under high-energy irradiation and give a valuable insight into the understanding of weak intermolecular interactions involving radicals relevant to atmospheric chemistry, combustion, and carbon dioxide conversion.

Impact of COVID-19 on admission and in-hospital mortality of patients with acute myocardial infarction in Korea: An interrupted time series analysis

Objectives The purpose of this study is to investigate the impact of COVID-19 on admission and in-hospital mortality of patients with acute myocardial infarction (AMI). Methods We constructed a dataset of monthly hospitalizations and mortality of inpatients with AMI from January 2017 to December 2021 utilizing the National Health Insurance Claims Data which covers nearly the entire population. Using an interrupted time series (ITS), we investigated how COVID-19 affected hospitalizations and in-hospital deaths of patients with AMI. Results During the study period, the average age of patients with AMI was 65.2–65.8 years, and the ratio of men to women was higher, with 73.0–75.3% of patients being men and 24.7–27.0% being women. ITS analysis showed that admission rates of patients with AMI decreased one per 100,000 population due to COVID-19 (P&lt;0.001). Reductions in admission rates were greatest among men, those aged 55 and older, and people with medical aid. COVID-19 did not affect inpatient mortality (p = 0.9608), but in-hospital mortality decreased from 12% to 7% in the medical aid group. Conclusion Overall, we found that COVID-19 had an impact on admission rates of patients with AMI but did not have a significant impact on in-hospital mortality. However, we also found differential impacts by sex, age, and socioeconomic status, indicating some may be more vulnerable. This highlights the importance of identifying and supporting these vulnerable populations to prevent poorer health outcomes.

Intradermally injected abobotulinumtoxinA administered preemptively before surgery alleviates post-surgical pain and normalizes behavior in a translational animal model

Rubber wear: Experiment and theory

We study the wear rate (mass loss per unit sliding distance) of a tire tread rubber compound sliding on concrete paver surfaces under dry and wet conditions, at different nominal contact pressures of σ0 = 0.12, 0.29, and 0.43 MPa, and sliding velocities ranging from v = 1 μm s−1 to 1 cm s−1. We find that the wear rate is proportional to the normal force and remains independent of the sliding speed. Sliding in water and soapy water results in significantly lower wear rates compared to dry conditions. The experimental data are analyzed using a theory that predicts wear rates and wear particle size distributions consistent with the experimental observations.

Identification of novel proteins associated with intelligence by integrating genome-wide association data and human brain proteomics

While genome-wide association studies (GWAS) have identified genetic variants associated with intelligence, their biological mechanisms remain largely unexplored. This study aimed to bridge this gap by integrating intelligence GWAS data with human brain proteomics and transcriptomics. We conducted proteome-wide (PWAS) and transcriptome-wide (TWAS) association studies, along with enrichment and protein-protein interaction (PPI) network analyses. PWAS identified 44 genes in the human brain proteome that influence intelligence through protein abundance regulation (FDR P &lt;  0.05). Causal analysis revealed 36 genes, including GPX1, involved in the cis-regulation of protein abundance (P &lt;  0.05). In independent PWAS analyses, 17 genes were validated, and 10 showed a positive correlation with intelligence (P &lt;  0.05). TWAS revealed significant SNP-based heritability for mRNA in 28 proteins, and cis-regulation of mRNA levels for 20 genes was nominally associated with intelligence (FDR P &lt;  0.05). This study identifies key genes that bridge genetic variants and protein-level mechanisms of intelligence, providing novel insights into its biological pathways and potential therapeutic targets.

Vertical structure of subsurface marine heatwaves in a shallow nearshore upwelling system

Abstract Marine heatwaves (MHWs) are increasing in frequency and intensity globally and are among the greatest threats to marine ecosystems. However, limited studies have characterized subsurface MHWs, particularly in shallow waters. We utilized nearly two decades of full water-column (~ 10 m) observations from a unique automated profiler in central California to characterize, for the first time, the vertical structure of MHWs in a shallow nearshore upwelling system. We found MHWs have similar average durations and intensities across all depths, but there were ~ 17% more bottom MHW days than surface MHW days. Nearly one third of bottom MHWs occurred independently of surface MHWs, indicating that satellites miss a significant fraction of events. MHWs showed distinct seasonality with more frequent and intense events during the fall/winter when weak stratification allowed for MHWs to occupy a larger portion of the water column and persist longer. During summer, strong stratification limited the vertical extent of MHWs, leading to surface- and bottom-trapped events with shorter durations and intensities. Additionally, MHW initiation and termination across depths was consistently linked to anomalously low and high coastal upwelling, respectively. This study highlights the need for expansion of subsurface monitoring of MHWs globally amid a warming planet.

Accurate prediction of electron correlation energies of topological atoms by delta learning from the Müller approximation

FFLUX is a polarizable machine-learning force field that deploys pre-trained kernel-based models of quantum topological properties in molecular dynamics simulations. Despite a track record of successful applications, this unconventional force field still uses Lennard-Jones parameters to account for dispersion effects when performing in-bulk simulations. However, optimal Lennard-Jones parameters are system-dependent and not easy to calibrate. Fortunately, physics-informed dispersion energies can be obtained from the two-particle density matrix (2PDM) of any system using correlated wavefunctions. The only challenge is that the 2PDM is a humongous object whose calculation is very time-consuming and memory-greedy. In this proof-of-concept study, we utilize the Δ-learning method to address both problems using a small set of water trimers. More specifically, we obtain pure two-electron correlation energies with the aug-cc-pVDZ basis set at the cost of Müller-approximated 2PDM calculated at a very small basis set, 6-31+G(d). We also benchmark different Δ-learning tasks designed by changing the baseline and target method and/or the basis set. Our experiments suggest that two-electron correlation energies of weakly relaxed water trimers can be accurately predicted via Δ-learning with a maximum absolute error of 1.30 ± 0.32 kJ/mol traded against a colossal computational speed-up of roughly 40 times.

Statin use and low-density lipoprotein cholesterol target achievement for primary prevention of atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus: a multicenter cross-sectional study in Sri Lanka

Background Statin therapy serves a crucial role as a primary preventive strategy against atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM). Even though diabetes poses a significant and growing health concern in Sri Lanka, there is a lack of information regarding the prevalence and intensity of statin prescriptions and the achievement of recommended LDL-C targets in diabetic patients for the primary prevention of ASCVD within the nation. We aimed to assess the prevalence and intensity of statin prescriptions, target LDL-C achievement, and factors associated with target LDL-C achievement for the primary prevention of ASCVD in T2DM patients across several tertiary care facilities in Sri Lanka. Methods A multi-centered, cross-sectional study was conducted among T2DM patients without clinical ASCVD attending six tertiary care medical clinics in Sri Lanka. Data on ASCVD risk factors and statin prescription were collected using an interviewer-administered questionnaire. ASCVD risk was calculated using the WHO charts. Atorvastatin 20 mg/ rosuvastatin 10 mg was defined as high-intensity statins and target LDL-C was defined as &lt; 70 mg/dL for moderate to high and &lt; 100 mg/dL for low-risk groups of ASCVD according to the NICE guideline. The independent sample t test, one-way ANOVA and chi-square test were used for data analysis as appropriate. Factors linked to achieving LDL-C targets were determined through multiple logistic regression analysis. Level of significance was considered as 0.05. Results Of the 2013 participants studied, 46.7% were at moderate-high risk and the rest were at low risk of ASCVD. All were eligible for statin therapy, and 84.1% were prescribed statins. High-intensity statins had been prescribed only for 38.5% of moderate-high-risk patients. Nonetheless, high-intensity statins have also been prescribed for 30.7% of low-risk patients. LDL-C target achievement was studied in a randomly selected subsample of 683 and 65.4% (70.7% in low-risk patients and 60.3% in moderate-high-risk patients) achieved LDL-C targets. Of moderate-high-risk patients, 46.3% had not achieved target LDL-C even with high-intensity statin therapy. Female gender (OR = 1.52, 95% CI 1.03-2.24, p = 0.036), poor adherence to statins (OR = 1.67, 95% CI 1.18-2.37, p = 0.004), poor glycemic control (OR = 2.27, 95% CI 1.41-3.65, p = 0.001), and inadequate physical activity (OR = 1.48, 95% CI 1.04-2.10, p = 0.031) were significantly associated with failing to achieve LDL-C targets. Conclusion Only about one third of diabetes patients with moderate-high ASCVD risk received high-intensity statins. Even with high-intensity statin therapy, nearly half of the treated patients failed to meet recommended LDL-C targets.

The molecular dynamic studies of thermal conductivity of SiC ceramic derived from β/α phase transformation

Numerical methods for unraveling inter-particle potentials in colloidal suspensions: A comparative study for two-dimensional suspensions

We compare three model-free numerical methods for inverting structural data to obtain interaction potentials, namely, iterative Boltzmann inversion (IBI), test-particle insertion (TPI), and a machine-learning (ML) approach called ActiveNet. Three archetypal models of two-dimensional colloidal systems are used as test cases: Weeks–Chandler–Anderson short-ranged repulsion, the Lennard-Jones potential, and a repulsive shoulder interaction with two length scales. Additionally, data on an experimental suspension of colloidal spheres are acquired by optical microscopy and used to test the inversion methods. The methods have different merits. IBI is the only choice when the radial distribution function is known but particle coordinates are unavailable. TPI requires snapshots with particle positions and can extract both pair- and higher-body potentials without the need for simulation. The ML approach can only be used when particles can be tracked in time and it returns the force rather than the potential. However, it can unravel pair interactions from any one-body forces (such as drag or propulsion) and does not rely on equilibrium distributions for its derivation. Our results may serve as a guide when a numerical method is needed for application to experimental data and as a reference for further development of the methodology itself.

Predicting Parkinson’s disease trajectory using clinical and functional MRI features: A reproduction and replication study

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual’s PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson’s Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 &gt; 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data