The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.

Many mammals can temporally uncouple conception from parturition by pacing down their development around the blastocyst stage. In mice, this dormant state is achieved by decreasing the activity of the growth-regulating mTOR signaling pathway. It is unknown whether this ability is conserved in mammals in general and in humans in particular. Here, we show that decreasing the activity of the mTOR signaling pathway induces human pluripotent stem cells (hPSCs) and blastoids to enter a dormant state with limited proliferation, developmental progression, and capacity to attach to endometrial cells. These in vitro assays show that, similar to other species, the ability to enter dormancy is active in human cells around the blastocyst stage and is reversible at both functional and molecular levels. The pacing of human blastocyst development has potential implications for reproductive therapies.

Background: IgE-mediated food allergy is the result of an aberrant immune response involving the interaction of a food allergen with its specific IgE bound to FceRI, the high affinity IgE receptor, on mast cells. Allergen-specific IgE also binds to soluble binding factors, but, their expression and role in food allergy is not well characterized. Here, we assess the prevalence and relevance of soluble IgE binding factors in food allergy and tolerance.

Methods: We measured serum levels of four IgE binding factors, that is, galectin-3, galectin-9, soluble Fc epsilon RI (sFc epsilon RI) and soluble CD23 (sCD23) in 67 adults sensitized to peanut or hazelnut and sFc epsilon RI in 29 children sensitized to hen's egg. Adults without food allergen sensitization (n = 17) served as healthy controls. We compared serum levels of patients and controls and assessed them, in the former, for links to clinical features including allergy and tolerance.

Results: Serum levels of sFc epsilon RI and sCD23, but not galectin-3 and galectin-9, significantly differ in food-sensitized patients as compared to healthy controls. A subgroup (28%) of peanut and hazelnut allergic patients had elevated sFc epsilon RI levels, that were associated with higher total and specific IgE levels. Furthermore, sFc epsilon RI levels were significantly higher in tolerant subjects compared to allergics. Among hazelnut allergic patients, those with high sFc epsilon RI levels tolerated the highest protein amounts in the oral food challenge.

Conclusion: sFc epsilon RI but not sCD23, galectin-3 and galectin-9 might play a role in the pathophysiology of food allergy. Its functional role or use as biomarker should be assessed in further studies.

A major objective of the strong ongoing drive to realize quantum simulators of gauge theories is achieving the capability to probe collider-relevant physics on them. In this regard, a highly pertinent and sought-after application is the controlled collisions of elementary and composite particles, as well as the scattering processes in their wake. Here, we propose particle-collision experiments in a cold-atom quantum simulator for a 1 +1⁢D (one spatial and one temporal dimension) U(1) lattice gauge theory with a tunable topological 𝜃 term, where we demonstrate an experimentally feasible protocol to impart momenta to elementary (anti)particles and their meson composites. We numerically benchmark the collisions of moving wave packets for both elementary and composite particles, uncovering a plethora of rich phenomena, such as oscillatory string dynamics in the wake of elementary (anti)particle collisions due to confinement. We also probe string inversion and entropy production processes across Coleman’s phase transition through far-from-equilibrium quenches. We further demonstrate how collisions of composite particles unveil their internal structure. Our work paves the way towards the experimental investigation of collision dynamics in state-of-the-art quantum simulators of gauge theories, and sets the stage for microscopic understanding of collider-relevant physics in these platforms.

One of the most dramatic changes occurring on our planet is the ever-increasing extensive use of artificial light at night, which drastically altered the environment to which nocturnal animals are adapted. Such light pollution has been identified as a driver in the dramatic insect decline of the past years. One nocturnal species group experiencing marked declines are moths, which play a key role in food webs and ecosystem services such as plant pollination. Moths can be easily monitored within the illuminated area of a streetlight, where they typically exhibit disoriented behavior. Yet, little is known about their behavior beyond the illuminated area. Harmonic radar tracking enabled us to close this knowledge gap. We found a significant change in flight behavior beyond the illuminated area of a streetlight. A detailed analysis of the recorded trajectories revealed a barrier effect of streetlights on lappet moths whenever the moon was not available as a natural celestial cue. Furthermore, streetlights increased the tortuosity of flights for both hawk moths and lappet moths. Surprisingly, we had to reject our fundamental hypothesis that most individuals would fly toward a streetlight. Instead, this was true for only 4% of the tested individuals, indicating that the impact of light pollution might be more severe than assumed to date. Our results provide experimental evidence for the fragmentation of landscapes by streetlights and demonstrate that light pollution affects movement patterns of moths beyond what was previously assumed, potentially affecting their reproductive success and hampering a vital ecosystem service.

Background

Antimicrobial resistance (AMR) in Enterobacterales constitutes a significant threat to the health of both humans and animals and a socioeconomic problem. Enterobacterales, mainly Escherichia coli, carrying β-lactamases has become one of the main indicators to estimate the burden of AMR in animals within “One Health” approach.

Objectives

To assess the presence of extended-spectrum cephalosporin-resistant Enterobacterales associated with ruminants (cattle, sheep, goats) habituated in all five provinces of Rwanda and to perform in depth characterization of isolates.

Methods

We screened 454 rectal swabs from 203 cows, 170 goats, and 81 sheep and selective isolation of extended-spectrum cephalosporin-resistant Enterobacterales was conducted. Isolates were identified as a members of the order Enterobacterales by MALDI-TOF MS and further characterized by susceptibility testing and by whole-genome sequencing.

Results

Out of the 454 samples, 64 extended-spectrum cephalosporin-resistant Enterobacterales were isolated from 58 animals. Isolates belonged to seven bacterial species and were identified as Escherichia coli (n = 54), Enterobacter bugandensis (n = 4), Enterobacter mori (n = 2), Klebsiella pneumoniae (n = 2), Enterobacter dykesii (n = 1), and Citrobacter freundii (n = 1). All isolates displayed an Extended-spectrum β-lactamases (ESBL) phenotype, with exception of Citrobacter freundii isolate displayed both an ESBL and AmpC phenotype. In addition, all Enterobacter isolates were identified as stably de-repressed AmpC-producers. ESBLs genes, blaCTX−M−15 was predominant. Resistance to tetracycline and tet(A) was most frequently observed among non-β-lactam resistance. Forty-eight isolates displayed multidrug-resistance phenotypes. A shiga toxin-producing E. coli and an enterotoxigenic E. coli isolate were observed. Genome comparisons revealed thirty-five E. coli sequence types (ST) (ST10, ST307 being predominate).

Conclusions

Considering the high proximity between ruminants and humans in Rwanda, the dissemination of antimicrobial drug resistance highlights the public health threats and requires the joint and multisectoral action of human and veterinary medicine, at human-animal-environment interfaces. Therefore, it is important to establish national and global “One Health” surveillance programs of AMR to tackle the antibiotic-resistant crisis in human and veterinary medicine.

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the “Sacrobosco Collection,” consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472–1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.

Adrenocortical carcinoma (ACC) is a rare yet devastating tumour of the adrenal gland with a molecular pathology that remains incompletely understood. To gain novel insights into the cellular landscape of ACC, we generated single-nuclei RNA sequencing (snRNA-seq) data sets from twelve ACC tumour samples and analysed these alongside snRNA-seq data sets from normal adrenal glands (NAGs). We find the ACC tumour microenvironment to be relatively devoid of immune cells compared to NAG tissues, consistent with known high tumour purity values for ACC as an immunologically “cold” tumour. Our analysis identifies three separate groups of ACC samples that are characterised by different relative compositions of adrenocortical cell types. These include cell populations that are specifically enriched in the most clinically aggressive and hormonally active tumours, displaying hallmarks of reorganised cell mechanobiology and dysregulated steroidogenesis, respectively. We also identified and validated a population of mitotically active adrenocortical cells that strongly overexpress genes POLQ, DIAPH3 and EZH2 to support tumour expansion alongside an LGR4+ progenitor-like or cell-of-origin candidate for adrenocortical carcinogenesis. Trajectory inference suggests the fate adopted by malignant adrenocortical cells upon differentiation is associated with the copy number or allelic balance state of the imprinted DLK1/MEG3 genomic locus, which we verified by assessing bulk tumour DNA methylation status. In conclusion, our results therefore provide new insights into the clinical and cellular heterogeneity of ACC, revealing how genetic perturbations to healthy adrenocortical renewal and zonation provide a molecular basis for disease pathogenesis.

Ongoing global warming is expected to augment soil respiration by increasing the microbial activity, driving self-reinforcing feedback to climate change. However, the compensatory thermal adaptation of soil microorganisms and substrate depletion may weaken the effects of rising temperature on soil respiration. To test this hypothesis, we collected soils along a large-scale forest transect in eastern China spanning a natural temperature gradient, and we incubated the soils at different temperatures with or without substrate addition. We combined the exponential thermal response function and a data-driven model to study the interaction effect of thermal adaptation and substrate availability on microbial respiration and compared our results to those from two additional continental and global independent datasets. Modeled results suggested that the effect of thermal adaptation on microbial respiration was greater in areas with higher mean annual temperatures, which is consistent with the compensatory response to warming. In addition, the effect of thermal adaptation on microbial respiration was greater under substrate addition than under substrate depletion, which was also true for the independent datasets reanalyzed using our approach. Our results indicate that thermal adaptation in warmer regions could exert a more pronounced negative impact on microbial respiration when the substrate availability is abundant. These findings improve the body of knowledge on how substrate availability influences the soil microbial community–temperature interactions, which could improve estimates of projected soil carbon losses to the atmosphere through respiration.

The impact of microplastics on antibiotic resistance has attracted widespread attention. However, previous studies primarily focused on the effects of individual microplastics. In reality, diverse microplastic types accumulate in soil, and it remains less well studied whether microplastic diversity (i.e., variations in color, shape or polymer type) can be an important driver of increased antibiotic resistance gene (ARG) abundance. Here, we employed microcosm studies to investigate the effects of microplastic diversity on soil ARG dynamics through metagenomic analysis. Additionally, we evaluated the associated potential health risks by profiling virulence factor genes (VFGs) and mobile genetic elements (MGEs). Our findings reveal that as microplastic diversity increases, there is a corresponding rise in the abundance of soil ARGs, VFGs and MGEs. We further identified microbial adaptive strategies involving genes (changed genetic diversity), community (increased specific microbes), and functions (enriched metabolic pathways) that correlate with increased ARG abundance and may thus contribute to ARG dissemination. Additional global change factors, including fungicide application and plant diversity reduction, also contributed to elevated ARG abundance. Our findings suggest that, in addition to considering contamination levels, it is crucial to monitor microplastic diversity in ecosystems due to their potential role in driving the dissemination of antibiotic resistance through multiple pathways.

As clinical trials for psychedelics move into phase III in the USA, Europe must address its lag in integrating professional education around psychedelic-assisted therapy (PAT) and supporting psychedelic drug research. This paper evaluates the necessary frameworks for implementing PAT in Germany, emphasizing the nation’s potential leadership role within the European Union. With Australia having already approved MDMA and psilocybin for mental health indications, the Ukrainian government exploring MDMA treatment for war-related PTSD, and initial clinical trials involving MDMA and LSD with patients in Switzerland which restarted the restricted medical use of these substances around 2014, the medical authorization of psychedelics in these countries establishes precedent showcasing both the promise and challenges of researching and implementing PAT in nations where the substances were formally scheduled as illicit substances. Key challenges include establishing rigorous standards for practitioner training, accessibility, and defining regulatory oversight. This paper focuses on the development of robust infrastructure in Germany, which will support the roll out of PAT, and details ethical considerations, training protocols, and governmental roles in the formulation of treatment frameworks. This approach aims not only to guide Germany in adopting PAT but also to influence broader European policy, ensuring that patients receive ethically sound and proficient care. The findings suggest pathways for Europe to reclaim its historical lead in psychiatric and therapeutic innovation.

Atom manipulation by scanning tunneling microscopy was used to construct quantum dots on the InAs(111)A surface. Each dot comprised six ionized indium adatoms. The positively charged adatoms create a confining potential acting on surface-state electrons, leading to the emergence of a bound state associated with the dot. By lining up the dots into 𝑁-dot chains with alternating tunnel coupling between them, quantum-dot molecules were constructed that revealed electronic boundary states as predicted by the Su-Schrieffer-Heeger (SSH) model of one-dimensional topological phases. Dot chains with odd 𝑁 were constructed such that they host a single end or domain-wall state, allowing one to probe the localization of the boundary state on a given sublattice by scanning tunneling spectroscopy. We found probability density also on the forbidden sublattice together with an asymmetric energy spectrum of the chain-confined states. This deviation from the SSH model arises because the dots are charged and create a variation in on-site potential along the chain—which does not remove the boundary states but shifts their energy away from the midgap position. Our results demonstrate that topological boundary states can be created in quantum-dot arrays engineered with atomic-scale precision.

While quantum state tomography is notoriously hard, most states hold little interest to practically minded tomographers. Given that states and unitaries appearing in nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with 𝐺 two-qubit gates to a small trace distance, a sample complexity scaling linearly in 𝐺 is necessary and sufficient. We also prove that the optimal query complexity to learn a unitary generated by 𝐺 gates to a small average-case error scales linearly in 𝐺. While sample-efficient learning can be achieved, we show that under reasonable cryptographic conjectures, the computational complexity for learning states and unitaries of gate complexity 𝐺 must scale exponentially in 𝐺. We illustrate how these results establish fundamental limitations on the expressivity of quantum machine-learning models and provide new perspectives on no-free-lunch theorems in unitary learning. Together, our results answer how the complexity of learning quantum states and unitaries relate to the complexity of creating these states and unitaries.

The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method’s learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available.

MASK-air®, a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Sante on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma.

Combining the frameworks of fundamental causes theory and diffusion of innovation, scholars had anticipated a delayed COVID-19 vaccination uptake for people in lower socioeconomic position depending on the socioeconomic context. We qualify these propositions and analyze educational differences in COVID-19 vaccination status over the first ten months of Germany’s vaccination campaign in 2021. Data from the study “Corona Monitoring Nationwide” (RKI-SOEP-2), collected between November 2021 and February 2022, is linked with district-level data of the German Index of Socioeconomic Deprivation (GISD). We estimated the proportion of people with at least one vaccination dose stratified by educational groups and within different settings of regional socioeconomic deprivation at three time points. Logistic multilevel regression models were applied to adjust for multiple covariates and to test cross-level-interactions between educational levels and levels of area-level socioeconomic deprivation. Vaccination rates were lower among respondents with lower education. With increasing area-level socioeconomic deprivation, educational differences were larger due to particularly low vaccination rates in groups with low education levels. The analysis of vaccination timing reveals that educational gaps and gaps by area-level socioeconomic deprivation had appeared early in the vaccination campaign and did not close completely before the 4th wave of COVID-19 infections.

For a given graph G = (V, E ), we define its nth subdivision as the graph obtained from G by replacing every edge by a path of length n . We also define the mth power of G as the graph on vertex set V where we connect every pair of vertices at distance at most m in G . In this paper, we study the chromatic number of powers of subdivisions of graphs and resolve the case m = n asymptotically. In particular, our result confirms a conjecture of Mozafari-Nia and Iradmusa in the case m = n = 3 in a strong sense.

Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.

Although C1 clasts in carbonaceous chondrites are usually mineralogically similar to CI chondrites, they often exhibit distinct chemical or isotope characteristics, indicating that the diversity of carbonaceous matter is larger than represented by currently known meteorites. Samples returned by the Hayabusa2 mission provide an excellent opportunity to directly compare remote sensing data with laboratory spectra and elaborate on meteorite-asteroid links. We obtained reflectance spectra from 10 carbonaceous samples of extraterrestrial origin to identify spectral differences in the wavelength region between 2.5 and 16.5 μm. We investigated seven volatile-rich clasts, two CI chondrites, and a fragment from the asteroid Ryugu, recently returned by the Hayabusa2 mission. To obtain representative spectra from a lithology, we performed multiple analysis with an aperture size of 100 μm × 100 μm. Subsequently, spectral features were correlated with petrographic and chemical data. The phyllosilicate composition of the investigated C1 and C2 clasts is on average more Fe-rich compared to bulk CI chondrites, which is spectrally reflected in lower Christiansen feature (CF)/Reststrahlenband (RB) ratios. Our results confirm previous studies that indicate that the band area of the OH absorption band at 2.7 μm is dependent on the phyllosilicate composition. A high Mg abundance in phyllosilicates leads to a stronger OH absorption band. Varying degrees of aqueous alteration cause mineralogic differences that are observable in the reflectance spectra. Either in form of a band center shift towards smaller or longer wavelengths, depending on the metal cation giving rise to the M-OH absorption band, and/or a generally weaker OH absorption band, and a broad Reststrahlen band (RB) at 10 μm, with two minor RBs emerging at 11.3 and 12 μm. In contrast, most C1 clasts show a single RB at ≈10 μm, and a constant OH band position at 2.70 μm. The abundance of minor constituents, such as sulfides and carbonates, can also affect the spectrum. Dolomite produces two diagnostic bands at 6.5 and 11.3 μm, whereas pyrrhotite, devoid of diagnostic bands in this wavelength region, increases the background while decreasing the RB intensity. Our findings indicate that within a laboratory framework, subtle mineralogic differences among hydrated carbonaceous materials can be spectroscopically detected. The spectra of Ryugu sample A0008 show a distinctive OH absorption band, as seen in the globally retrieved data by the NIRS3 instrument for Ryugu (Kitazato et al., 2019). Under specific circumstances, micro-FTIR reflectance spectra can be qualitatively compared to remote sensing spectra, and help to further elaborate on meteorite-asteroid links.