IST Austria: PubRep (Institute of Science and Technology)
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Information processing in biochemical networks
No full textLiving systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components
Chromatin landscape is associated with sex-biased expression and Drosophila-like dosage compensation of the Z chromosome in Artemia franciscana
No full textThe males and females of the brine shrimp Artemia franciscana are highly dimorphic, and this dimorphism is associated with substantial sex-biased gene expression in heads and gonads. How these sex-specific patterns of expression are regulated at the molecular level is unknown. A. franciscana also has differentiated ZW sex chromosomes, with complete dosage compensation, but the molecular mechanism through which compensation is achieved is unknown. Here, we conducted CUT&TAG assays targeting 7 post-translational histone modifications (H3K27me3, H3K9me2, H3K9me3, H3K36me3, H3K27ac, H3K4me3, and H4K16ac) in heads and gonads of A. franciscana, allowing us to divide the genome into 12 chromatin states. We further defined functional chromatin signatures for all genes, which were correlated with transcript level abundances. Differences in the occupancy of the profiled epigenetic marks between sexes were associated with differential gene expression between males and females. Finally, we found a significant enrichment of the permissive H4K16ac histone mark in the Z-specific region in both tissues of females but not males, supporting the role of this histone mark in mediating dosage compensation of the Z chromosome
Direct and efficient detection of quantum superposition
No full textOne of the most striking quantum phenomena is superposition, where one particle simultaneously inhabits different states. Most methods to verify coherent superposition are indirect, in that they require the distinct states to be recombined. Here, we adapt an xor game, in which a “test” photon is placed in a superposition of two orthogonal spatial modes, and each mode is sent to separated parties who perform local measurements on their modes without reinterfering the original modes. We show that by using a second identical “measurement” photon the parties are nonetheless able to verify if the test photon was placed in coherent superposition of the two spatial modes. We then turn this game into a resource-efficient verification scheme, obtaining a confidence that the particle is superposed which approaches unity exponentially fast. We demonstrate our scheme using a single photon, obtaining a 99% confidence that the particle is superposed with only 37 copies. Our work shows the utility of xor games to verify quantum resources, allowing us to efficiently detect quantum superposition without reinterfering the superposed modes
Asymptotic growth of translation-dilation orbits
No full textBy studying some Clausen-like multiple Dirichlet series, we complete the proof of Manin's conjecture for sufficiently split smooth equivariant compactifications of the translation-dilation group over the rationals. Secondary terms remain elusive in general
LNCS
No full textStatistical model checking estimates probabilities and expectations of interest in probabilistic system models by using random simulations. Its results come with statistical guarantees. However, many tools use unsound statistical methods that produce incorrect results more often than they claim. In this paper, we provide a comprehensive overview of tools and their correctness, as well as of sound methods available for estimating probabilities from the literature. For expected rewards, we investigate how to bound the path reward distribution to apply sound statistical methods for bounded distributions, of which we recommend the Dvoretzky-Kiefer-Wolfowitz inequality that has not been used in SMC so far. We prove that even reachability rewards can be bounded in theory, and formalise the concept of limit-PAC procedures for a practical solution. The modes SMC tool implements our methods and recommendations, which we use to experimentally confirm our results
Evidence of ferroelectric distortions in topological crystalline insulators via transverse thermoelectric measurements
No full textThe transverse thermoelectric (Nernst) effect is a powerful probe for studying the electronic and structural properties of materials. In this study, we employ transverse thermoelectric measurements to investigate the ferroelectric distortion in the topological crystalline insulator (TCI) Pb0.60Sn0.40Te, a compound derived from PbTe and SnTe, known for their exceptional thermoelectric performance and distinct ferroelectric properties. By leveraging Nernst measurements, we provide direct evidence of ferroelectric distortion in this TCI, corroborated by Shubnikov–de Haas quantum oscillations that confirm the presence of two topologically nontrivial Fermi pockets. Density functional theory calculations show that these pockets originate from the L and T points in the Brillouin zone of the distorted structure within the TCI phase. Raman spectroscopy further identifies a structural phase transition below 50 K, consistent with the quantum oscillation observations. This observation is further substantiated by temperature-dependent synchrotron X-ray pair distribution function analysis and transmission electron microscopy, which confirm the local off-centering of cations at low temperature. These findings underscore the potential of transverse thermoelectric measurements in unveiling ferroelectric distortions and their role in modulating topological quantum states, opening new directions for research into the synergy between ferroelectricity and topological phases
Synthesis of selectively 13C/2H/15N- labeled arginine to probe protein conformation and interaction by NMR spectroscopy
No full textThe charged arginine side chain is unique in determining many innate properties of proteins, contributing to stability and interaction surfaces, and directing allosteric regulation and enzymatic catalysis. NMR experiments can be used to reveal these processes at the molecular level, but it often requires selective insertion of carbon-13, nitrogen-15, and deuterium at defined atomic positions. We introduce a method to endow arginine residues with defined isotope patterns, combining synthetic organic chemistry and cell-based protein overexpression. The resulting proteins feature NMR active spin systems with optimized relaxation pathways leading to simplified NMR spectra with a sensitive response to changes in the chemical environment of the nuclei observed
The mass distribution of stars stripped in binaries: The effect of metallicity
No full textStars stripped of their hydrogen-rich envelopes through binary interaction are thought to be responsible for both hydrogen-poor supernovae and the hard ionizing radiation observed in low-Z galaxies. A population of these stars was recently observed for the first time, but their prevalence remains unknown. In preparation for such measurements, we estimate the mass distribution of hot, stripped stars using a population synthesis code that interpolates over detailed single and binary stellar evolution tracks. We predict that for a constant star formation rate of 1 M⊙/yr and regardless of metallicity, a scalable model population contains ∼30 000 stripped stars with mass Mstrip > 1 M⊙ and ∼4000 stripped stars that are sufficiently massive to explode (Mstrip > 2.6 M⊙). Below Mstrip = 5 M⊙, the distribution is metallicity-independent and can be described by a power law with the exponent α ∼ −2. At higher masses and lower metallicity (Z ≲ 0.002), the mass distribution exhibits a drop. This originates from the prediction, frequently seen in evolutionary models, that massive low-metallicity stars do not expand substantially until central helium burning or later and therefore cannot form long-lived stripped stars. With weaker line-driven winds at low metallicity, this suggests that neither binary interaction nor wind mass loss can efficiently strip massive stars at low metallicity. As a result, a “helium-star desert” emerges around Mstrip = 15 M⊙ at Z = 0.002, covering an increasingly large mass range with decreasing metallicity. We note that these high-mass stars are those that potentially boost a galaxy’s He+-ionizing radiation and that participate in the formation of merging black holes. This “helium-star desert” therefore merits further study
How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons
No full textWe investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict nucleus independent chemical shifts (NICS) in the space around the molecule. We train a series of models, each time reducing the size of the largest molecules used in training. The accuracy of prediction remains high (MAE < 0.5 ppm), even when training the model only on molecules with up to four rings, thus providing strong evidence for the locality of magnetic response. To overcome the known problem of generalization of GNNs, we implement a k-hop expansion strategy and succeed in achieving accurate predictions for molecules with up to 15 rings (almost 4 times the size of the largest training example). Our findings have implications for understanding the magnetic response in complex molecules and demonstrate a promising approach to overcoming GNN scalability limitations. Furthermore, the trained models enable rapid characterization, without the need for more expensive DFT calculations
Let us FIGURE it out: Why do scientists still make “bad” figures?
No full textIn an era of high-resolution displays, powerful design software, and automated plotting tools, one would think that scientific figures would be clearer than ever. Yet, despite numerous editorials, guidelines, and workshops dedicated to improving figure design, poorly constructed figures remain a persistent issue. Editors and experienced researchers have repeatedly highlighted key pitfalls such as cluttered layouts, inconsistent formatting, poor color choices, and misleading visuals. (1−8) Yet, the aforementioned graphical shortcomings continue to plague even high-impact journals. Why? The problem is not a lack of technology; it is a combination of poor design habits, rushed deadlines, and a tendency to treat figures as mere “data dumps” rather than as essential storytelling tools.
Many people process information more effectively through visuals, naturally associating concepts easily when presented graphically. A well-crafted figure serves as a narrative within the larger story, making complex ideas more accessible. Unfortunately, visual storytelling often takes a backseat in scientific communication. Scientists are trained to analyze and interpret data, but many default to software-generated plots without considering accessibility or how their figures will be perceived by readers outside their immediate field. Without thoughtful design, figures lose their power to enhance understanding, ultimately limiting the significance of the research itself.
In this editorial, we examine the challenges that, in our view, hamper scientific figure design and discuss how thoughtful refinements driven by feedback, iteration, and design principles can enhance clarity and impact visual communication