Background

Cefazolin is used as a prophylactic antibiotic to reduce surgical site infections (SSIs). Obesity has been identified as a risk factor for SSIs. Cefazolin dosing recommendations and guidelines are currently inconsistent for obese patients. As plasma and target-site exposure might differ, pharmacokinetic data from the sites of SSIs are essential to evaluate treatment efficacy: these data can be obtained via tissue microdialysis. This analysis was designed to evaluate the need for dosing adaptations in obese patients for surgical prophylaxis.

Methods

Data from 15 obese (BMImedian = 52.6 kg m−2) and 15 age- and sex-matched nonobese patients (BMImedian = 26.0 kg m−2) who received 2 g cefazolin i.v. infusion for infection prophylaxis were included in the analysis. Pharmacokinetic data from plasma and interstitial space fluid (ISF) of adipose tissue were obtained and analysed simultaneously using nonlinear mixed-effects modelling. Dosing regimens were evaluated by calculating the probability of target attainment (PTA) and the cumulative fraction of response (CFR) for plasma and ISF using unbound cefazolin concentration above minimum inhibitory concentration 100% of the time as target (fT>MIC = 100%). Dosing regimens were considered adequate when PTA and CFR were ≥90%.

Results

Evaluation of cefazolin doses of 1 and 2 g with redosing at either 3 or 4 h by PTA and CFR in plasma and ISF found 2 g cefazolin with redosing at 4 h to be the most suitable dosing regimen for both obese and nonobese patients (PTA >90% and CFR >90% for both).

Conclusions

This model-based analysis, using fT>MIC = 100% as a target, showed that cefazolin dosing adaptations are not required for surgical prophylaxis in obese patients.

Surface melting supports the development of pigmented algal blooms on the Greenland Ice Sheet, decreasing albedo and further accelerating melting. The interplay between carbon-fixing algae and carbon-respiring heterotrophic microorganisms ultimately controls the amount and composition of organic matter (OM) and thus the ice and snow color. Yet, the dynamics of microbially-derived OM on the Greenland Ice Sheet remain unclear. To address this knowledge gap, we incubated in situ algae-dominated snow and ice samples under light and dark conditions and characterized the changes in dissolved and particulate OM (DOM and POM) with the help of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry. We show that glacier ice-algae habitats are dominated by highly unsaturated and aromatic compounds resistant to bio- and photo-degradation. In contrary, snow-algae habitats are enriched in bioavailable and more photosensitive unsaturated aliphatics and sulfur- and phosphorus-containing compounds. In both habitats, light exposure increased water-soluble DOM compounds derived from POM, which accounted for ~ 50–70% of the initial DOM composition. Of the initial DOM, 35–50% were heterotrophically degraded in the dark, while light alone photodegraded 6–16%. The significant accumulation of light-absorbing aromatics from POM and DOM at the end of the ice-algae experiments, underscore the greater impact of glacier ice-algae habitats on altering glacier color and accelerating melting.

Red snow algae bloom at the surface of snowfields worldwide, and their detection is relevant for ecological, biogeochemical and mass balance studies. In this study, we co-located RGB imagery acquired with a light-weight Uncrewed Aerial Vehicle (UAV) to 129 hyperspectral reflectance spectra from which the snow surface properties were retrieved, thereby enabling high-resolution aerial mapping of algal properties. We present maps of red snow algae abundance and albedo reducing effect over ∼ 9700 m2 of seasonal snowfields across Hardangervidda, Southern Norway, in July and August 2023. The average albedo reducing effect of the algae over the entire area was 0.012 ± 0.005, and attained 0.028 ± 0.004 on a snowfield of ∼ 710 m2. Across snow surfaces with visible blooms only, the algal albedo reducing effect was 0.045 ± 0.003, equivalent to an additional ∼ 3 mm of daily melting under local illumination conditions, and aggregating to 5,500 ± 2,300 kg of daily snowmelt. The intensity and spatial coverage of surface algal blooms were very variable between and within the individual snowfields. Analysis of the UAV imagery suggests that multiple small and distributed samples are at least twice more likely to yield representative estimates of the average snow algal concentration of a snowfield compared to fewer, larger samples. Our study demonstrates the potential of low-cost and easy to deploy UAVs for red snow algal monitoring at the cm to sub-cm scale, which can be used to better understand their spatial ecology and role in albedo reduction.

Phytochromes are red-light-sensitive biliprotein photoreceptors that control a variety of physiological processes in plants, fungi, and bacteria. Lately, greater attention has been paid to these photoreceptors due to their potential as fluorescent probes for deep-tissue microscopy. Such fluorescing phytochromes have been generated by multiple amino acid substitutions in weakly fluorescent wild-type (WT) proteins. Remarkably, the single substitution of conserved Tyr176 by His in cyanobacterial phytochrome Cph1 increases the fluorescence quantum yield from 2.4 to 14.5%. In this work, we studied this Y176H variant by crystallography, MAS NMR, resonance Raman spectroscopy, and ultrafast absorption spectroscopy complemented by theoretical methods. Two factors were identified to account for the strong fluorescence increase. First, the equilibrium between the photoactive and fluorescent substates of WT Cph1 was shown to shift entirely to the fluorescent substate in Y176H. Second, structural flexibility of the chromophore is drastically reduced and the photoisomerization barrier is raised, thereby increasing the excited-state lifetime. The most striking finding, however, is that Y176H includes the structural properties of both the dark-adapted Pr and the light-activated Pfr state. While the chromophore adopts the Pr-typical ZZZssa configuration, the tongue segment of the protein adopts a Pfr-typical α-helical structure. This implies that Tyr176 plays a key role in coupling chromophore photoisomerization to the sheet-to-helix transition of the tongue and the final Pfr structure. This conclusion extends to plant phytochromes, where the homologous substitution causes light-independent signaling activity akin to that of Pfr.

Silicate weathering reactions generate alkalinity that drives carbonate burial in the ocean, ultimately considered to balance solid-earth CO2 degassing. Because silicate weathering occurs at a climate dependent rate, it provides a negative feedback on long-term climate evolution that regulates Earth's temperature within a habitable window. Quantifying the effect of silicate weathering on CO2 sequestration is challenging, because an unknown fraction of the alkalinity is consumed by the formation of marine authigenic clays. This process – termed reverse weathering – counteracts the degassing-weathering feedback loop and retains carbon in the ocean-atmosphere system. Quantifying reverse weathering is challenging due to the difficulties in detecting and quantifying marine authigenic aluminosilicate clays (MAAC) in marine sediments and distinguishing them from byproducts of alkalinity generating marine (forward) silicate weathering reactions. Here, a comparison of the lithium contents and isotope3 ratios of the clay-sized fraction of marine and fluvial sediments along Chile suggests that higher lithium contents and 7Li/6Li ratios of marine detrital sediments compared to their fluvial counterparts are diagnostic of the presence of MAACs. The increases in Li content and isotope ratios were used to derive a lithium isotope fractionation of ca. −30‰ associated with the formation of MAACs in sediments derived from continents. Using this new constraint on authigenic clay formation, we reevaluate the global lithium budget and derive a flux of lithium associated with MAAC formation in marine detrital sediments.

Childbirth-related posttraumatic stress symptoms (CB-PTSS) affect around 12% of postpartum individuals. While the subjective experience of childbirth is a key predictor of CB-PTSS, the specific defining characteristics of negative birth experiences remain poorly understood. This study aims to refine our understanding of negative birth experiences by investigating intrapartum hotspots, i.e. moments of extreme distress, and their association with CB-PTSS. In a cross-sectional study of N = 1,140 individuals who had given birth eight to ten weeks before, we examined the following: the types of hotspots, differences in hotspot-related distress, interpersonal difficulties during the hotspot, and CB-PTSS between the various types of hotspots and whether hotspot-related distress and interpersonal difficulties independently predicted CB-PTSS. Participants completed several items based on previous qualitative work [Citation1] to assess the worst hotspot, hotspot-related distress, and interpersonal difficulties during the hotspot and the City Birth Trauma Scale to measure CB-PTSS, alongside relevant pregnancy- and birth-related questions. Medical interventions were the most frequently experienced worst hotspot and separation from the child was associated with the highest levels of hotspot-related distress, interpersonal difficulties, and CB-PTSS. Hotspot-related distress and interpersonal difficulties independently predicted CB-PTSS. Examining intrapartum hotspots poses a promising approach to define highly negative birth experiences.

The synthesis of an arsanyl-phosphagallene [H2CN(Dipp)]2AsP[double bond, length as m-dash]Ga(NacNac) (NacNac = HC[C(Me)N(Dipp)]2; Dipp = diisopropylphenyl) and its reactivity towards heterocumulenes and ketones is described. Reactions with azides, carbodiimides, isocyanates and ketones give rise to heterocycles via cyclization reactions involving the Ga[double bond, length as m-dash]P π-bond (with the Ga–P σ-bond remaining unperturbed in the final products). By contrast, reactions with CO2, CS2 and COS are more intriguing, revealing a reactivity profile in which the phosphorus atom can abstract carbon monoxide from the oxygen-containing heterocumulenes. These reactions result in the formation of gallium phosphaethynolate compounds. Such reactivity is enabled by the presence of a weakly Lewis basic arsanyl moiety which, in contrast to other related compounds featuring phosphanyl groups, is insufficiently nucleophilic to play a role in frustrated Lewis-pair like reactivity.

Unsupervised learning has become an essential building block of artifical intelligence systems. The representations it produces, for example, in foundation models, are critical to a wide variety of downstream applications. It is therefore important to carefully examine unsupervised models to ensure not only that they produce accurate predictions on the available data but also that these accurate predictions do not arise from a Clever Hans (CH) effect. Here, using specially developed explainable artifical intelligence techniques and applying them to popular representation learning and anomaly detection models for image data, we show that CH effects are widespread in unsupervised learning. In particular, through use cases on medical and industrial inspection data, we demonstrate that CH effects systematically lead to significant performance loss of downstream models under plausible dataset shifts or reweighting of different data subgroups. Our empirical findings are enriched by theoretical insights, which point to inductive biases in the unsupervised learning machine as a primary source of CH effects. Overall, our work sheds light on unexplored risks associated with practical applications of unsupervised learning and suggests ways to systematically mitigate CH effects, thereby making unsupervised learning more robust.

Online portals have facilitated collecting extensive biodiversity data by naturalists, offering unprecedented coverage and resolution in space and time. Despite being the most widely available class of biodiversity data, opportunistically collected records have remained largely inaccessible to community ecologists since the imperfect and highly heterogeneous detection process can severely bias inference. We present a novel statistical approach that leverages these datasets by embedding a spatiotemporal joint species distribution model within a flexible site-occupancy framework. Our model addresses variable detection probabilities across visits and species by modelling phenological patterns and by extending the use of latent variables to characterise observer-specific detection and reporting behaviour. We apply our model to an opportunistically collected dataset on lentic odonates, encompassing over 100,000 waterbody visits in Flanders (N-Belgium), to show that the model provides insights into biological communities at high resolution, including phenology, interannual trends, environmental associations and spatiotemporal co-distributional patterns in community composition.

In today’s digital age, fake news has become a major problem with serious consequences, ranging from social unrest to political upheaval. New methods for detecting and mitigating fake news are required to address this issue. In this work, we propose incorporating contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural NeTwork Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch combines Word and Transformer embeddings with a Deep Neural Network architecture based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) to capture contextual features and generate a Text Content Embedding. This integrated approach allows for a more comprehensive understanding of the textual information. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE’s Ensemble module combines the Text Content and Propagation Embeddings, to create a powerful and unique Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that this approach improves fake news detection and outperforms state-of-the-art models.

Mine tailings generated from hydrometallurgical processing of nickel–cobalt laterite deposits contain high levels of chromium (Cr), with the hexavalent species being a toxic pollutant and carcinogen. However, the partitioning, speciation, and local bonding environment of Cr in the mine tailings remain largely unknown, hindering our ability to predict its toxicity and long-term behavior. Coupling detailed mineralogical, spectroscopic, and geochemical characterization with sequential extraction of tailings from active and rehabilitated dams, we show that Cr is present in its least toxic form, Cr(III), and largely immobilized by recalcitrant minerals. This immobilization also regulates dissolved Cr concentrations in the interacting waters to levels up to five times lower than the global regulatory limit (50 μg L–1). Solid-phase Cr concentrations were ≤1.5 wt % with 39–61% of Cr incorporated into hematite, and to a lesser extent, alunite, both of which formed early in the hydrometallurgical extraction process of mined laterite ores. The remaining Cr was present as recalcitrant chromite residues from the primary source laterites. We highlight that, although hydrometallurgical extractions liberate Cr from laterite ores during processing, they also provide ideal chemical pathways for the formation of highly stable, crystalline hematite that successfully sequesters Cr, while restricting its environmental mobility.

Learning accurate beliefs about the world is computationally demanding but critical for adaptive behavior across the lifespan. Here, we build on an established framework formalizing learning as predictive inference and examine the possibility that age differences in learning emerge from efficient computations that consider available cognitive resources differing across the lifespan. In our resource-rational model, beliefs are updated through a sampling process that stops after reaching a criterion level of accuracy. The sampling process navigates a trade-off between belief accuracy and computational cost, with more samples favoring belief accuracy and fewer samples minimizing costs. When cognitive resources are limited or costly, a maximization of the accuracy–cost ratio requires a more frugal sampling policy, which leads to systematically biased beliefs. Data from two lifespan studies (N = 129 and N = 90) and one study in younger adults (N = 94) show that children and older adults display biases characteristic of a more frugal sampling policy. This is reflected in (a) more frequent perseveration when participants are required to update from previous beliefs and (b) a stronger anchoring bias when updating beliefs from an externally generated value. These results are qualitatively consistent with simulated predictions of our resource-rational model, corroborating the assumption that the identified biases originate from sampling. Our model and results provide a unifying perspective on perseverative and anchoring biases, show that they can jointly emerge from efficient belief-updating computations, and suggest that resource-rational adjustments of sampling computations can explain age-related changes in adaptive learning.

Pigments played a vital technological role by enabling the development of advanced artistic techniques, preserving cultural heritage through durable materials like frescoes and facilitating innovations in early chemistry, such as the creation of synthetic colouring compounds. This paper examines pigments found in some exceptional Pompeian contexts spanning the 3rd century BCE to the 79 CE eruption, covering almost the entire palette of an ancient painter made of natural and synthetic, inorganic and organic pigments. Their composition has been revealed thanks to a non-invasive analytical approach designed to preserve these invaluable archaeological resources, illuminating that the artists skillfully mixed the colouring materials to achieve an uncountable range of colour tones. Quantifying any individual colouring compound enables a review of recipes as reported by ancient sources and modern scientific literature and opens new scenarios in the artistic process that likely started in the pigmentarium. In the analysis of the mixtures, the role of Egyptian blue and red lead in the variation of shades, which are almost ubiquitous as additional components in paint mixtures, is worth noting. Ultimately, one of the samples uncovered the earliest known use of a light green compound containing baryte and alunite, providing the first definitive evidence of barium sulphate being utilized in the Mediterranean during ancient times.

The emplacement of dikes and sills plays a crucial role in crustal mechanics. The parameter used to describe their resistance to propagation, fracture energy, remains controversial. Here, we show how different stress biaxiality levels experienced by dikes can directly affect the micromechanisms of crack propagation in rocks, consequently impacting fracture energy. We performed controlled tensile crack propagation experiments under opposite stress biaxialities. We connect fracture energy variations monitored through a compliance-based method to crack microstructures observed on post-mortem specimens. Microscopy techniques showed that biaxial tension generates intricate microstructures driven by topological instabilities, such as deflections and branches, to circumnavigate tougher grains. This yields a higher fracture energy, that we attribute to front roughening and bridging mechanisms due to front fragmentation. Bridging toughening is gradual and increase with crack size. This hints at the existence of a scale dependency of fracture energy of dikes also experiencing biaxial tension.

Quantum complexity measures the difficulty of realizing a quantum process, such as preparing a state or implementing a unitary. We present an approach to quantifying the thermodynamic resources required to implement a process if the process’s complexity is restricted. We focus on the prototypical task of information erasure, or Landauer erasure, wherein an 𝑛-qubit memory is reset to the all-zero state. We show that the minimum thermodynamic work required to reset an arbitrary state in our model, via a complexity-constrained process, is quantified by the state’s complexity entropy. The complexity entropy therefore quantifies a trade-off between the work cost and complexity cost of resetting a state. If the qubits have a nontrivial (but product) Hamiltonian, the optimal work cost is determined by the complexity relative entropy. The complexity entropy quantifies the amount of randomness a system appears to have to a computationally limited observer. Similarly, the complexity relative entropy quantifies such an observer’s ability to distinguish two states. We prove elementary properties of the complexity (relative) entropy. In a random circuit—a simple model for quantum chaotic dynamics—the complexity entropy transitions from zero to its maximal value around the time corresponding to the observer’s computational-power limit. Also, we identify information-theoretic applications of the complexity entropy. The complexity entropy quantifies the resources required for data compression if the compression algorithm must use a restricted number of gates. We further introduce a complexity conditional entropy, which arises naturally in a complexity-constrained variant of information-theoretic decoupling. Assuming that this entropy obeys a conjectured chain rule, we show that the entropy bounds the number of qubits that one can decouple from a reference system, as judged by a computationally bounded referee. Overall, our framework extends the resource-theoretic approach to thermodynamics to integrate a notion of time, as quantified by complexity.

Protothecosis is a rare and unusual disease that affects both humans and animals, including dogs. The causative agents are unicellular, achlorophyllous, “yeast-like” microalgae of the genus Prototheca (Trebouxiophyceae, Chlorophyta). Although usually saprophytic, Prototheca may, under conditions of immunologic compromise, become pathogenic and even lethal to the host. We present a synthesis of the current literature on protothecosis, with special emphasis on disease features in the dog. Five open-access scientific journal repositories were searched two times by two independent reviewers for original studies (including case reports, standard articles, and conference abstracts) pertaining to cases of protothecosis in dogs. Findings about protothecosis cases in dogs (e.g., animal metrics, type of infection, implemented treatment, and treatment outcome) were synthesized in independent data tables. Eighty studies describing 125 cases of protothecosis in dogs qualified for final analysis. Based on this investigation, protothecosis in dogs can be defined as an emerging disease that poses a serious challenge to the veterinary profession in terms of both diagnosis and management. In general, clinical signs and physical findings most often are referable to the gastrointestinal tract (n = 68; 54.4%). Yet the most common clinical manifestation in dogs is disseminated systemic infection (n = 84; 67.2%), including clinical signs referable to inflammation affecting more than one organ. We emphasize the complexity of Prototheca infection in dogs by summarizing clinical and laboratory findings from 125 cases of Prototheca infection in dogs published over the last half-century.

Livestock production systems are complex and evolve over time, affecting their adaptability to economic, political, and disease-related changes. In Europe, disease resilience is crucial due to threats like the African swine fever virus, which jeopardizes pork production stability. The European Union identifies farm production type as a key risk factor for disease spread, making it important to track changes in farm production types to assess disease risk. However, detailed production type data is often lacking in national databases. For Swiss pig farms, we used prediction and clustering algorithms to classify 9’687 − 11’247 trading farms between 2014 and 2019 by one of eleven production types. We then analyzed the pig trade network and stratified farm centrality measures (ICC and OCC) by production type. We found that 145 farms belonging to three production types have substantially higher ICC and OCC than other farms, suggesting that they could be the target of disease surveillance programs. Our predictions until 2025 show an increase both in overall pig trade network connectivity and in proportion of production types with high ICC and OCC, indicating that the structural changes in the Swiss pig production system may increase infectious disease exposure over time.

Mucus supports human health by hydrating, lubricating, and preventing infection of wet epithelial surfaces. The beneficial material properties and bioactivity of mucus stem from glycoproteins called mucins, motivating the development of mucin-derived hydrogels for wound dressings and antifouling coatings. However, these applications require robust gelation and adhesion to a wide range of substrates. Inspired by the chemical cross-linking and water-tolerant adhesion of marine mussel adhesive structures, we use catechol–thiol bonding to drive gelation of native mucin proteins and synthetic mucin-inspired polymers, forming soft, adhesive hydrogels that can be coated onto diverse surfaces. The gelation dynamics and adhesive properties can be systematically tuned by varying the hydrogel composition, polymer architecture, and thiol availability, with gelation timescales adjustable from seconds to hours, and values of elastic modulus, failure stress, and debonding work spanning orders of magnitude. We demonstrate the functionality of these gels in two applications: as tissue adhesives, using porcine skin as a proxy for human skin, and as bioactive surface coatings to prevent bacterial colonization. The results highlight the potential of catechol–thiol cross-linking as a versatile platform for engineering multifunctional glycoprotein hydrogels with applications in wound repair and antimicrobial surface engineering.

Glycerol, often considered a waste byproduct of biodiesel production, holds the potential for conversion into chemicals of varying economic value, such as dihydroxyacetone (DHA) and formic acid (FA). Hence, accurate identification and quantification of glycerol oxidation reaction (GOR) products are crucial for glycerol valorization research and practical deployment. High-performance liquid chromatography (HPLC) is the preferred analytical method for these purposes due to its proficiency in separating and quantifying components in liquid mixtures, even in the presence of diluted solutes. On the other hand, peak overlap in chromatograms, especially among glycerol, DHA, and FA, poses a notable challenge in the analysis of GOR products. This study introduces a quantification method aimed at resolving peak overlaps in HPLC analysis of GOR products. Initially, we examine the optical properties of glycerol and GOR products to identify optimal wavelengths for spectrophotometric HPLC analysis and detection. Subsequently, we propose an algebraic approach to resolve the peak overlap of glycerol, DHA, and FA using various detectors, including the refractive index detector (RID) and the variable wavelength detector (VWD). This method is applied to analyze the GOR products of undoped, nonco-catalyzed nanoporous BiVO4 photoanodes, which have shown an intrinsic catalytic activity toward GOR products in previous studies.

The study examined the vitamin A status in commercially managed suckler cows and lactating dairy cows and identified primary influencing factors. Liver retinyl ester concentrations were higher in multiparous than primiparous cows (p < 0.01). Pasture availability was associated with higher β-carotene concentrations (p < 0.001). In dairy cows, pasture access during the dry period did not affect any of the parameters assayed. β-Carotene and retinol in milk increased with parity. No vitamin A deficiency or hypervitaminosis A was detected. Liver and milk retinol and retinyl ester concentrations that were analysed in the present study and data from a recent German total diet study were used to estimate the exposure to preformed vitamin A in vulnerable groups (children, 0.5–5 years). 95th percentiles of preformed vitamin A intake do not exceed tolerable upper intake levels in individuals between 1 year and 5 years, but in infants 6 to 12 months of age.