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Motivation Discovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources. Results Here we introduce AlphaFastPPi, a pipeline built upon AlphaFold-Multimer and AlphaPulldown. AlphaFastPPi is a streamlined pipeline which reduces resource consumption compared to other methods resulting in enhanced efficiency. This is achieved by lowering the minimum number of models required from five to one, while maintaining a good accuracy. This provides a more sustainable approach to PPI prediction. We demonstrate that this optimization does not compromise the accuracy of the method, offering a fast, efficient, and environmentally friendly solution for PPI predictions.

The distribution of masses of neutron stars, particularly the maximum mass value, is considered a probe of their formation, evolution and internal physics (i.e., equation of state). This mass distribution could in principle be inferred from the detection of gravitational waves from binary neutron star mergers. Using mock catalogues of $10^5$ dark sirens events, expected to be detected by Einstein Telescope over an operational period of $\sim1\, \rm year$ , we show how the biased luminosity distance measurement induced by gravitational lensing affects the inferred redshift and mass of the merger. This results in higher observed masses than expected. Up to $2\%$ of the events are predicted to fall above the maximum allowed neutron star mass depending on the intrinsic mass distribution and signal-to-noise ratio threshold adopted. The underlying true mass distribution and maximum mass could still be approximately recovered in the case of bright standard sirens.

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein’s activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

We pose the problem of metastability for a three--state spin system with conservative dynamics. We consider the Blume--Capel model with the Kawasaki dynamics, we prove that, in a particular region of the parameter plane, the metastable state is the unique homogeneous minus state, and we estimate the exit time. To achieve our goal we have to solve several variational problems in the configuration space which result to be particularly involved, due to complicated structure of the trajectories. They key ingredient is the control of the energy differences between the configurations crossed when a spin is transported from the boundary to an internal site of the lattice through a completely arbitrary mixture of the three--state spin species. To master these mechanisms we have introduced a new approach based on the transport of spins along nearest neighbor connected regions of the lattice with constant spin configuration. This novel approach goes beyond the Blume--Capel model and can be used for the study of more general multi--state spin models.

Popular word embedding methods such as GloVe and Word2Vec are related to the factorization of the pointwise mutual information (PMI) matrix. In this paper, we link correspondence analysis (CA) to the factorization of the PMI matrix. CA is a dimensionality reduction method that uses singular value decomposition (SVD), and we show that CA is mathematically close to the weighted factorization of the PMI matrix. In addition, we present variants of CA that turn out to be successful in the factorization of the word-context matrix, i.e. CA applied to a matrix where the entries undergo a square-root transformation (ROOT-CA) and a root-root transformation (ROOTROOT-CA). An empirical comparison among CA- and PMI-based methods shows that overall results of ROOT-CA and ROOTROOT-CA are slightly better than those of the PMI-based methods.

Breast cancer is the second most common cancer globally. Most deaths from breast cancer are due to metastatic disease which often follows long periods of clinical dormancy 1. Understanding the mechanisms that disrupt the quiescence of dormant disseminated cancer cells (DCC) is crucial for addressing metastatic progression. Infection with respiratory viruses (e.g. influenza or SARS-CoV-2) is common and triggers an inflammatory response locally and systemically 2,3. Here we show that influenza virus infection leads to loss of the pro-dormancy mesenchymal phenotype in breast DCC in the lung, causing DCC proliferation within days of infection, and a greater than 100-fold expansion of carcinoma cells into metastatic lesions within two weeks. Such DCC phenotypic change and expansion is interleukin-6 (IL-6)-dependent. We further show that CD4 T cells are required for the maintenance of pulmonary metastatic burden post-influenza virus infection, in part through attenuation of CD8 cell responses in the lungs. Single-cell RNA-seq analyses reveal DCC-dependent impairment of T-cell activation in the lungs of infected mice. SARS-CoV-2 infected mice also showed increased breast DCC expansion in lungs post-infection. Expanding our findings to human observational data, we observed that cancer survivors contracting a SARS-CoV-2 infection have substantially increased risks of lung metastatic progression and cancer-related death compared to cancer survivors who did not. These discoveries underscore the significant impact of respiratory viral infections on the resurgence of metastatic cancer, offering novel insights into the interconnection between infectious diseases and cancer metastasis.

Decision-making processes often involve voting. Human interactions with exogenous entities such as legislations or products can be effectively modeled as two-mode (bipartite) signed networks-where people can either vote positively, negatively, or abstain from voting on the entities. Detecting communities in such networks could help us understand underlying properties: for example ideological camps or consumer preferences. While community detection is an established practice separately for bipartite and signed networks, it remains largely unexplored in the case of bipartite signed networks. In this paper, we systematically evaluate the efficacy of community detection methods on bipartite signed networks using a synthetic benchmark and real-world datasets. Our findings reveal that when no communities are present in the data, these methods often recover spurious communities. When communities are present, the algorithms exhibit promising performance, although their performance is highly susceptible to parameter choice. This indicates that researchers using community detection methods in the context of bipartite signed networks should not take the communities found at face value: it is essential to assess the robustness of parameter choices or perform domain-specific external validation.

Technology-Assisted Review (TAR) aims to reduce the human effort required for screening processes such as abstract screening for systematic literature reviews. Human reviewers label documents as relevant or irrelevant during this process, while the system incrementally updates a prediction model based on the reviewers' previous decisions. After each model update, the system proposes new documents it deems relevant, to prioritize relevant documentsover irrelevant ones. A stopping criterion is necessary to guide users in stopping the review process to minimize the number of missed relevant documents and the number of read irrelevant documents. In this paper, we propose and evaluate a new ensemble-based Active Learning strategy and a stopping criterion based on Chao's Population Size Estimator that estimates the prevalence of relevant documents in the dataset. Our simulation study demonstrates that this criterion performs well on several datasets and is compared to other methods presented in the literature.

The FLAG-tag/anti-FLAG system is a widely used biochemical tool for protein detection and purification. Anti-FLAG M2 is the most popular antibody against the FLAG-tag, due to its ease of use, versatility, and availability in pure form or as bead conjugate. M2 binds N-terminal, C-terminal and internal FLAG-tags and binding is calcium-independent, but the molecular basis for the FLAG-tag specificity and recognition remains unresolved. Here we present an atomic resolution (1.17 Å) structure of the FLAG peptide in complex with the Fab of anti-FLAG M2, revealing key binding determinants. Five of the eight FLAG peptide residues form direct interactions with paratope residues. The FLAG peptide adopts a 310 helix conformation in complex with the Fab. These structural insights allowed us to rationally introduce point mutations on both the peptide and antibody side. We tested these by surface plasmon resonance, leading us to propose a shorter yet equally binding version of the FLAG-tag for the M2 antibody.

Background: Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g., genomics), and another defining a system-wide expression response (e.g., transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation. Results: We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes. Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely underreported. Additionally, CIBRA can identify impact with only ten cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Conclusions: Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response. CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA

Oncogene-induced replication stress (RS) is a vulnerability of cancer cells that forces reliance on the intra-S-phase checkpoint to ensure faithful genome duplication. Inhibitors of the crucial intra-S-phase checkpoint kinases ATR and CHK1 have been developed, but persistent proliferation and resistance to these drugs remain problematic. Understanding drug tolerance mechanisms is impeded by analysis of bulk samples, which neglect tumor heterogeneity and often fail to accurately interpret cell cycle-mediated resistance. Here, by combining intracellular immunostaining and RNA-sequencing of single cells, we characterized the transcriptomes of oncogenic RAS-expressing cells that exhibit variable levels of RS when challenged with a CHK1 inhibitor in combination with the chemotherapeutic drug gemcitabine. We identified 40 genes differentially expressed between tolerant and sensitive cells, including several FOXM1 target genes. While complete knockdown of FOXM1 impeded cell proliferation, a partial knockdown protected cells against DNA damage, and improved recovery from drug-induced RS. Our results suggest that low levels of FOXM1 expression protects subsets of oncogenic RAS-expressing cells against DNA damage during drug-induced replication stress.

Affine Diffusion dynamics are frequently used for Valuation Adjustments (xVA) calculations due to their analytic tractability. However, these models cannot capture the market-implied skew and smile, which are relevant when computing xVA metrics. Hence, additional degrees of freedom are required to capture these market features. In this paper, we address this through an SDE with state-dependent coefficients. The SDE is consistent with the convex combination of a finite number of different AD dynamics. We combine Hull-White one-factor models where one model parameter is varied. We use the Randomized AD (RAnD) technique to parameterize the combination of dynamics. We refer to our SDE with state-dependent coefficients and the RAnD parametrization of the original models as the rHW model. The rHW model allows for efficient semi-analytic calibration to European swaptions through the analytic tractability of the Hull-White dynamics. We use a regression-based Monte-Carlo simulation to calculate exposures. In this setting, we demonstrate the significant effect of skew and smile on exposures and xVAs of linear and early-exercise interest rate derivatives.

People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of ‘ambiguous figures’ such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salient.

Street View-level Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillary, but due to the heterogeneity of the images, these require substantial preprocessing, filtering, and careful quality checks. We present an efficient method for automated downloading, processing, cropping, and filtering open SVI, to be used in a survey of human perceptions of the streets portrayed in these images. We demonstrate our open-source reusable SVI preparation and smartphone-friendly perception-survey software with Amsterdam (Netherlands) as the case study. Using a citizen science approach, we collected from 331 people 22,637 ratings about their perceptions for various criteria. We have published our software in a public repository for future re-use and reproducibility.

Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. The Monte Carlo (MC) method remains the prevalent choice for pricing engines; however, its slow convergence rate impedes its practical application. Fourier methods leverage the knowledge of the characteristic function to accurately and rapidly value options with up to two assets. Nevertheless, they face hurdles in the high-dimensional settings due to the tensor product (TP) structure of commonly employed quadrature techniques. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over MC or TP in the Fourier domain, and over MC in the physical domain for options with up to 15 assets.

Our understanding of past evolutionary change is often based on reconstructions based on incomplete data, raising fundamental questions about the degree to which we can make reliable inferences about past evolutionary processes. This was demonstrated by Louca and Pennell (2020), who showed that each pure-birth process can be generated by an infinite number of birth-death processes. Here, I explore what it means to reconstruct past evolutionary change with three approaches from measure theory, group theory, and homotopy theory to better understand structural constraint and origins of (non)identifiability. As an example, the developed framework is applied to the case of birth-death processes.

Background: While use of artificial intelligence (AI)-based technologies such as decision-support systems (AI-DSSs) could help sustaining and improving the quality and efficiency of care, their deployment also creates ethical and social challenges. In recent years, there has been a growing prevalence of high-level guidelines and frameworks to provide guidance on responsible AI innovation. However, few studies specify how AI-based technologies such as AI-DSSs can be responsibly embedded in specific contexts such as the nursing process in the long-term care (LTC) for older adults. Objective: Opportunities and prerequisites for responsible AI-assisted decision-making in the nursing process were explored from the perspectives of nurses and other professional stakeholders in LTC. Methods: Semi-structured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists and care centralists. Two imaginary scenarios about the future use of AI-DSSs were developed beforehand and used to enable participants to articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. After first openly discussing opportunities and possible risks associated with both scenarios, six high-level principles for responsible AI were used as probing themes to evoke further consideration on risks of using AI-DSSs in LTC. Further, participants were asked to brainstorm about possible strategies and actions in the design, implementation and use of AI-DSSs to address or mitigate the mentioned risks. A thematic analysis was carried out to identify opportunities and prerequisites for responsible innovation in this area. Results: Professionals’ stance towards the use of AI-DSSs is not a matter of purely positive or negative expectations, but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of opportunities and prerequisites f

Using the framework of ambidexterity developed by Hopkins and Lurie, we introduce a parametrized analogue of higher semiadditivity called $\mathcal Q$-semiadditivity, depending on a chosen class of morphisms $\mathcal Q$. Our first main result identifies the free $\mathcal Q$-semiadditive parametrized category on a single generator with a certain parametrized span category $\underline{\mathrm{Span}}(\mathcal Q)$, simultaneously generalizing a result of Harpaz in the non-parametrized setting and a result of Nardin in the equivariant setting. As a consequence, we deduce that the $\mathcal Q$-semiadditive completion of a parametrized category $\mathcal C$ consists of the $\mathcal Q$-commutative monoids in $\mathcal C$, defined as $\mathcal Q$-limit preserving parametrized functors from $\underline{\mathrm{Span}}(\mathcal Q)$ to $\mathcal C$. As our second main result, we provide an explicit `Mackey sheaf' description of the free presentable $\mathcal Q$-semiadditive category. Using this, we reprove the Mackey functor description of global spectra first obtained by the second-named author and generalize it to $G$-global spectra. Moreover, we obtain universal characterizations of the categories of $\mathbb Z$-valued $G$-Mackey profunctors and of quasi-finitely genuine $G$-spectra as studied by Kaledin and Krause-McCandless-Nikolaus, respectively.

We prove that commutative semirings in a cartesian closed presentable $\infty$-category, as defined by Groth, Gepner, and Nikolaus, are equivalent to product-preserving functors from the $(2,1)$-category of bispans of finite sets. In other words, we identify the latter as the Lawvere theory for commutative semirings in the $\infty$-categorical context. This implies that connective commutative ring spectra can be described as grouplike product-preserving functors from bispans of finite sets to spaces. A key part of the proof is a localization result for $\infty$-categories of spans, and more generally for $\infty$-categories with factorization systems, that may be of independent interest.

Fluidic iontronics is emerging as a distinctive platform for implementing neuromorphic circuits, characterized by its reliance on the same aqueous medium and ionic signal carriers as the brain. Drawing upon recent theoretical advancements in both iontronic spiking circuits and in dynamic transport of aqueous electrolytes through conical ion channels, which form fluidic memristors, we expand the repertoire of proposed neuronal spiking dynamics in iontronic circuits. Through a modelled circuit containing channels that carry a bipolar surface charge, we extract phasic bursting, mixed-mode spiking, tonic bursting, and threshold variability, all with spike voltages and frequencies within the typical range for mammalian neurons. These features are possible due to the strong dependence of the typical conductance memory retention time on the channel length, enabling timescales varying from individual spikes to bursts of multiple spikes within a single circuit. These advanced forms of neuronal-like spiking support the exploration of aqueous iontronics as an interesting platform for neuromorphic circuits.

Keratan sulfate (KS) is a proteoglycan that is widely expressed in the extracellular matrix of various tissue types where it performs multiple biological functions. KS is the least understood proteoglycan, which in part is due to a lack of panels of well-defined KS oligosaccharides that are needed for structure-binding studies, as analytical standards, to examine substrate specificities of keratinases and for drug development. Here, we report a biomimetic approach that makes it possible to install, in a regioselective manner, sulfates and fucosides on oligo-N-acetyllactosamine (LacNAc) chains to provide any structural element of KS by using specific enzyme modules. It is based on the observation that 1,3-fucosides, 2,6-sialosides and C-6 sulfation of galactose (Gal6S) are mutually exclusive and cannot occur on the same LacNAc moiety. As a result, the pattern of sulfation on galactosides can be controlled by installing1,3-fucosides or 2,6-sialosides to temporarily block certain LacNAc moieties from sulfation by keratan sulfate galactose 6-sulfotransferase (CHST1). The pattern of 1,3-fucosylation and 2,6-sialylation can be controlled by exploiting the mutual exclusivity of these modifications, which in turn controls the sites of sulfation by CHST1. Late-stage treatment with a fucosidase or sialidase to remove blocking fucosides or sialosides provides selectively sulfated KS oligosaccharides. These treatments also unmasked specific galactosides for further controlled modification by CHST1. To showcase the potential of the enzymatic strategy, we have prepared a range of poly-LacNAc derivatives having different patterns of fucosylation and sulfation and several N-glycans decorated by specific arrangements of sulfates.

Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2, necessitating a conversion to NOx. Previous studies applied a constant NO2-to-NOx conversion factor. In this paper, we develop a more realistic model for NO2 to NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH Large Eddy Simulation model with chemistry for the power plants Bełchatów (PL), Jänschwalde (DE), Matimba and Medupi (ZA), as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO, NO2, and NOx line densities from simulated NO and NO2 columns and derived NO2-to-NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions as well as that the conversion factors can be modeled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2-to-NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between -50 and -42 % to only -9.5 to -0.5 % in comparison with reported emissions. Single-overpass estimates can be quantifie

This work contributes to an European Monitoring and Verification Support (MVS) capacity for anthropogenic CO2 emissions. Future satellite instruments that map CO2 and NO2 from space will focus on hot-spot emissions from cities and large point sources, where CO2 emissions are accompanied by emissions of NOx. To use NOx as proxy CO2 emission, information about its atmospheric lifetime and the fraction of NOx present as NO2 is required to interpret NO2 plumes. This paper presents Large Eddy Simulations with atmospheric chemistry of four large point sources world-wide. We find that the chemical evolution of the plumes depends strongly on the amount of NOx that is emitted, next to wind speed and direction. For large NOx emissions the chemistry is pushed in a high-NOx chemical regime over a length of almost 100 km downwind of the stack location. Other plumes with lower NOx emissions show a fast transition to an intermediate NOx chemical regime, with short NOx lifetimes. Simulated NO2 columns mostly agree within 20 % with the TROPOspheric Monitoring Instrument (TROPOMI), signalling that the emissions used in the model were approximately correct. However, variability in the simulations is large, making a one-to-one comparison difficult. We find that wind speed variations should be accounted for in emission estimation methods. Moreover, results indicate that common assumptions about the NO2 lifetime (≈4 hours) and NOx: NO2 ratios (≈1.3) in simplified methods that estimate emissions from NO2 satellite data (e.g. Beirle et al., 2019) need revision.

Regulators aim to influence behavior of regulatees, such as compliance (i.e., following rules and regulations), but also ethical behavior (i.e., doing the right thing, irrespective of the rules and regulations). A literature review was conducted to collect, summarize, and analyze empirical evidence on how regulators can stimulate regulatees’ compliance and ethical behavior. We introduce a novel framework, in which we propose that regulatory actions influence compliance and ethical behavior through regulatees’ capability, opportunity, and motivation. Combining the findings of 35 articles, we showed that studies on ‘sanctions’ and 'cooperation' demonstrated mixed results regarding their effectiveness, whereas ‘inspections’ were found more effective. Notably, the subcomponents psychological capability, social opportunity, and reflective motivation were more effective in stimulating behavior than physical capability, physical opportunity, and automatic motivation. We reflect on how these insights can be used by regulators to increase their effectiveness, as well as for the aim to further develop regulatory theory.

The multicomponent coagulation equation is a generalisation of the Smoluchowski coagulation equation in which size of a particle is described by a vector. As with the original Smoluchowski equation, the multicomponent coagulation equation features gelation when supplied with a multiplicative kernel. Additionally, a new type of behaviour called localization is observed due to the multivariate nature of the particle size distribution. Here we extend and apply the branching process representation technique, which we introduced to study differential equations in our previous work, to find a concise probabilistic solution of the multicomponent coagulation equation supplied with monodisperse initial conditions and provide short proofs for the gelation time and localization.

Transcriptional enhancers orchestrate cell type- and time point-specific gene expression programs. Evolution of enhancer sequences can alter target gene expression without causing detrimental misexpression in other contexts. It has long been thought that this modularity allows evolutionary changes in enhancers to escape pleiotropic constraints, which is especially important for evolutionary constrained developmental patterning genes. However, there is still little data supporting this hypothesis. Here we identified signatures of accelerated evolution in conserved enhancer elements across the mammalian phylogeny. We found that pleiotropic genes involved in gene regulatory and developmental processes were enriched for accelerated sequence evolution within their enhancer elements. These genes were associated with an excess number of enhancers compared to other genes, and due to this they exhibit a substantial degree of sequence acceleration over all their enhancers combined. We provide evidence that sequence acceleration is associated with turnover of regulatory function. We studied one acceleration event in depth and found that its sequence evolution led to the emergence of a new enhancer activity domain that may be involved in the evolution of digit reduction in hoofed mammals. Our results provide tangible evidence that enhancer evolution has been a frequent contributor to modifications involving constrained developmental signaling genes in mammals.

Current Influenza virus vaccines primarily induce antibody responses against variable epitopes in hemagglutinin (HA), necessitating frequent updates. However, antibodies against neuraminidase (NA) can also confer protection against influenza, making NA an attractive target for the development of novel vaccines. In this study, we aimed to enhance the immunogenicity of recombinant NA antigens by presenting them multivalently on a nanoparticle carrier. Soluble tetrameric NA antigens of the N1 and N2 subtypes, confirmed to be correctly folded by cryo-electron microscopy structural analysis, were conjugated to Mi3 self-assembling protein nanoparticles using the SpyTag-SpyCatcher system. Immunization of mice with NA-Mi3 nanoparticles induced higher titers of NA-binding and -inhibiting antibodies and improved protection against a lethal challenge compared to unconjugated NA. Additionally, we explored the co-presentation of N1 and N2 antigens on the same Mi3 particles to create a mosaic vaccine candidate. These mosaic nanoparticles elicited antibody titers that were similar or superior to the homotypic nanoparticles and effectively protected against H1N1 and H3N2 challenge viruses. The NA-Mi3 nanoparticles represent a promising vaccine candidate that could complement HA-directed approaches for enhanced potency and broadened protection against influenza A virus.

We study a variant of the searching problem where the environment consists of a known terrain and the goal is to obtain visibility of an unknown target point on the surface of the terrain. The searcher starts on the surface of the terrain and is allowed to fly above the terrain. The goal is to devise a searching strategy that minimizes the competitive ratio, that is, the worst-case ratio between the distance traveled by the searching strategy and the minimum travel distance needed to detect the target. For $1.5$D terrains we show that any searching strategy has a competitive ratio of at least $\sqrt{82}$ and we present a nearly-optimal searching strategy that achieves a competitive ratio of $3\sqrt{19/2} \approx \sqrt{82} + 0.19$. This strategy extends directly to the case where the searcher has no knowledge of the terrain beforehand. For $2.5$D terrains we show that the optimal competitive ratio depends on the maximum slope $\lambda$ of the terrain, and is hence unbounded in general. Specifically, we provide a lower bound on the competitive ratio of $\Omega(\sqrt{\lambda})$. Finally, we complement the lower bound with a searching strategy based on the maximum slope of the known terrain, which achieves a competitive ratio of $O(\sqrt{\lambda})$.

In psychological science, replicability—repeating a study with a new sample achieving consistent results (Parsons et al., 2022)—is critical for affirming the validity of scientific findings. Despite its importance, replication efforts are few and far between in psychological science with many attempts failing to corroborate past findings. This scarcity, compounded by the difficulty in accessing replication data, jeopardizes the efficient allocation of research resources and impedes scientific advancement. Addressing this crucial gap, we present the Replication Database (https://metaanalyses.shinyapps.io/replicationdatabase/), a novel platform hosting 1,239 original findings paired with replication findings. The infrastructure of this database allows researchers to submit, access, and engage with replication findings. The database makes replications visible, easily findable via a graphical user interface, and tracks replication rates across various factors, such as publication year or journal. This will facilitate future efforts to evaluate the robustness of psychological research.

We extend a result by Gallow concerning the impossibility of following two epistemic masters, so that it covers a larger class of pooling methods. We also investigate a few ways of avoiding the issue, such as using non-convex pooling methods, employing the notion of imperfect trust or moving to higher-order probability spaces. Along the way we suggest a conceptual issue with the conditions used by Gallow: whenever two experts are considered, whether we can trust one of them is decided by the features of the other!

Stephen Hawking's derivation of Hawking radiation relied on one particular spacetime model, that of a star collapsing into a black hole which then remains in existence forever. He then argued that Hawking radiation implies this model should be thrown away in favour of a different model, that of an evaporating black hole. This aspect of Hawking's argument is an example of an idealization that is pervasive in the literature on black hole thermodynamics, but which has not yet been widely discussed by philosophers. The aim of this paper is to clarify the nature of Hawking's idealization, and to show a sense in which it leads to a paradox. After identifying this idealization paradox in classic derivations of Hawking radiation, I go on to show how various research programmes in black hole thermodynamics can be viewed as possible resolutions to the paradox. I give an initial analysis of the prospects for success of these various resolutions, and show how they shed light on both the philosophical foundations of both Hawking radiation on the nature of idealizations in physics.

Reconstructions of quantum theory are a novel research program in theoretical physics which aims to uncover the unique physical features of quantum theory via axiomatization. I focus on Hardy’s “Quantum Theory from Five Reasonable Axioms” (2001), arguing that reconstructions represent a modern usage of axiomatization with significant points of continuity to von Neumann’s axiomatizations in quantum mechanics. In particular, I show that Hardy and von Neumann share similar methodological ordering, have a common operational framing, and insist on the empirical basis of axioms. In the reconstruction programme, interesting points of discontinuity with historical axiomatizations include the stipulation of a generalized space of theories represented by a framework and the stipulation of analytic machinery at two levels of generality (first by establishing a generalized mathematical framework and then by positing specific formulations of axioms). In light of the reconstruction programme, I show that we should understand axiomatization attempts as being context-dependent, context which is contingent upon the goals of inquiry and the maturity of both mathematical formalism and theoretical underpinnings within the area of inquiry. Drawing on Mitsch (2022)’s account of axiomatization, I conclude that reconstructions should best be understood as provisional, practical, representations of quantum theory that are well suited for theory development and exploration. However, I propose my context-dependent re-framing of axiomatization as a means of enriching Mitsch’s account.

Recent advances in stem cell-derived human brain organoids and microelectrode array (MEA) technology raise profound questions about the potential for these systems to give rise to sentience. Brain organoids are 3D tissue constructs that recapitulate key aspects of brain development and function, while MEAs enable bidirectional communication with neuronal cultures. As brain organoids become more sophisticated and integrated with MEAs, the question arises: Could such a system support not only intelligent computation, but subjective experience? This paper explores the philosophical implications of this thought experiment, considering scenarios in which brain organoids exhibit signs of sensory awareness, distress, preference, and other hallmarks of sentience. It examines the ethical quandaries that would arise if compelling evidence of sentience were found in brain organoids, such as the moral status of these entities and the permissibility of different types of research. The paper also explores how the phenomenon of organoid sentience might shed light on the nature of consciousness and the plausibility of artificial sentience. While acknowledging the speculative nature of these reflections, the paper argues that the possibility of sentient brain organoids deserves serious consideration given the rapid pace of advances in this field. Grappling with these questions proactively could help set important ethical boundaries for future research and highlight critical avenues of scientific and philosophical inquiry. The thought experiment of sentient brain organoids thus serves as a valuable lens for examining deep issues at the intersection of neuroscience, ethics, and the philosophy of mind.

Within the context of general relativity, Leibnizian metaphysics seems to demand that worlds are "maximal" with respect to a variety of spacetime properties (Geroch 1970; Earman 1995). Here, we explore maximal worlds with respect to the "Heraclitus" asymmetry property which demands that of no pair of spacetime events have the same structure (Manchak and Barrett 2023). First, we show that Heraclitus-maximal worlds exist and that every Heraclitus world is contained in some Heraclitus-maximal world. This amounts to a type of compatibility between the Leibnizian and Heraclitian demands. Next, we consider the notion of "observationally indistinguishable" worlds (Glymour 1972, 1977; Malament 1977). We know that, modulo modest assumptions, any world is observationally indistinguishable from some other (non-isomorphic) world (Manchak 2009). But here we show a way out of this general epistemic predicament: if attention is restricted to Heraclitus-maximal worlds, then worlds are observationally indistinguishable if and only if they are isomorphic. Finally, we show a sense in which cosmic underdetermination can still arise for individual observers even if the Leibnizian and Heraclitian demands are met.

I consider the classical (i.e., non-relativistic) limit of Teleparallel Gravity, a relativistic theory of gravity that is empirically equivalent to General Relativity and features torsional forces. I show that as the speed of light is allowed to become infinite, Teleparallel Gravity reduces to Newtonian Gravity without torsion. I compare these results to the torsion-free context and discuss their implications on the purported underdetermination between Teleparallel Gravity and General Relativity. I conclude by considering alternative approaches to the classical limit developed in the literature.

If an asymmetry in time does not arise from the fundamental dynamical laws of physics, it may be found in special boundary conditions. The argument normally goes that since thermodynamic entropy in the past is lower than in the future according to the Second Law of Thermodynamics, then tracing this back to the time around the Big Bang means the universe must have started off in a state of very low thermodynamic entropy: the Thermodynamic Past Hypothesis. In this paper, we consider another boundary condition that plays a similar role, but for the decoherent arrow of time, i.e. the quantum state of the universe is more mixed in the future than in the past. According to what we call the Entanglement Past Hypothesis, the initial quantum state of the universe had very low entanglement entropy. We clarify the content of the Entanglement Past Hypothesis, compare it with the Thermodynamic Past Hypothesis, and identify some challenges and open questions for future research.

This chapter reviews empirical research on the rules governing assertion and retraction, with a focus on the normative role of truth. It examines whether truth is required for an assertion to be considered permissible, and whether there is an expectation that speakers retract statements that turn out to be false. Contrary to factive norms (such as the influential “knowledge norm”), empirical data suggests that there is no expectation that speakers only make true assertions. Additionally, contrary to truth-relativist accounts, there is no requirement for speakers to retract statements that are false at the context of assessment. We conclude by suggesting that truth still plays a crucial role in the evaluation of assertions: as a standard for evaluating their success, rather than permissibility.

Among the various attempts to formulate a theory of quantum gravity, a class of approaches suggests that spacetime, as modeled by general relativity, is destined to fade away. A major issue becomes then to identify which structures may inhabit the more fundamental, non-spatiotemporal environ- ment, as well as to explain the relationship with the higher-level spatiotemporal physics. Recently, it has been suggested that a certain understanding of functionalism is the proper tool to suitably ac- count for the recovery of spacetime. Here the viability and usefulness of such a conceptual strategy is explored, by looking at the various levels of spacetime emergence a theory of quantum gravity is expected to deal with. Our conclusion will be that, while its viability is clear also in a quantum gravity context, the import of spacetime functionalism remains rather unsettled.

This paper does two things. First, it reviews the recent debate between Halvorson and Manchak (2022) and Menon and Read (2024), looking for a reading of the former that is sympathetic to the concerns of the latter. Second, it considers whether there is a notion of determinism for spacetime theories that is adequate for the purposes of (Halvorson & Manchak, 2022); it concludes that there is not, but that we learn much of interest by considering the question.