@phdthesis{22255, abstract = {This thesis studies spectral rigidity and nonrigidity phenomena in dynamical systems. The central question is whether a dynamical system can be determined, up to a natural conjugacy, from its spectrum. We consider three related spectra: the length spectrum, the action spectrum, and the Lyapunov spectrum. The first part of the thesis concerns Liouville metrics on the two-dimensional torus. It is a long-standing folklore conjecture that Liouville metrics are the only integrable metrics on the torus. We prove a length-spectral rigidity result for linear conformal deformations of Liouville metrics by exploiting the dynamical properties of the rational tori -- analogues of the resonant convex caustics in billiards. We also establish a complementary classification result showing that marked-length-isospectral Liouville metrics are characterized by rearrangements of the one-dimensional functions appearing in their conformal factors, generalizing a theorem of Abbondandolo-Mazzucchelli. In particular, the second result gives nonrigidity examples within the class of Liouville metrics. The second part of the thesis studies the standard map from the viewpoint of action and Lyapunov spectra. We construct nontrivial deformations of the standard map which preserve the symplectic actions (respectively, the Lyapunov exponents) of infinitely many periodic orbits accumulating on an invariant curve. The proof combines a resonant normal form construction with Picard iteration schemes to obtain a sequence of periodic orbits accumulating on an invariant curve with a Liouville rotation number. Within the resonant normal forms we capture the dependence of these periodic orbits on the resonant Fourier coefficients of the dynamics on the invariant curve and, using the contraction mapping principle, obtain a suitable deformation achieving the prescribed spectral data associated with this sequence of orbits. The result can be viewed as a symplectic twist-map analogue of a length-spectral nonrigidity phenomenon for Riemannian manifolds and convex billiards, and it motivates the existence problem for similar 'partially length-isospectral' deformations of strictly convex billiard tables. }, author = {Li, Yunzhe}, issn = {2663-337X}, pages = {131}, publisher = {Institute of Science and Technology Austria}, title = {{Spectral rigidity and nonrigidity of dynamical systems}}, doi = {10.15479/AT-ISTA-22255}, year = {2026}, } @phdthesis{22873, author = {Ehrmann, Andreas}, isbn = {978-3-99078-092-3}, issn = {2663-337X}, keywords = {PhD Thesis, functional nanomachines, biological functionality, nanotechnology, energy delivery, target behavior, dynamics, design principles, optimization, differentiable statistical physics, machine learning}, pages = {168}, publisher = {Institute of Science and Technology Austria}, title = {{Biological functionality without biochemistry: Designing nanomachines for target behavior}}, doi = {10.15479/AT-ISTA-22873}, year = {2026}, } @phdthesis{23145, author = {Randuch, Marek}, issn = {2663-337X}, pages = {289}, publisher = {Institute of Science and Technology Austria}, title = {{Fast growth responses in plants: Growth regulation and phospho-proteomic }}, doi = {10.15479/AT-ISTA-23145}, year = {2026}, } @phdthesis{20551, abstract = {The space of codimension-2 shapes, such as curves in 3D and surfaces in 4D, is an infinite-dimensional manifold. This thesis explores geometric structures and dynamics on this space, with emphasis on their implications for physics, particularly hydrodynamics. Our investigation ranges from theoretical studies of infinite-dimensional symplectic and prequantum geometry to numerical computation of the time evolution of shapes. The thesis presents four main contributions. In the first part, we introduce implicit representations of codimension-2 shapes using a class of complex-valued functions, and prove that the space of these implicit representations forms a prequantum bundle over the codimension-2 shape space. This reveals a new geometric interpretation of the canonical symplectic structure on the codimension-2 shape space. In the second part, we use implicit representations to develop a simulation method for the dynamics of space curves. To handle chaotic systems such as vortex filaments in hydrodynamics, we exploit the infinite degrees of freedom, hidden in both the configuration and dynamics of implicit representations. In the third part, we introduce new symplectic structures on the space of space curves, which generalize the only previously known symplectic structure on this space, allowing for new Hamiltonian dynamics of space curves. In the fourth part, we apply a symplectic viewpoint to a differential geometric problem with practical applications. We derive a new area formula for spherical polygons via prequantization. }, author = {Ishida, Sadashige}, isbn = {978-3-99078-070-1}, issn = {2663-337X}, pages = {141}, publisher = {Institute of Science and Technology Austria}, title = {{Symplectic-prequantum structures and dynamics on the codimension-2 shape space}}, doi = {10.15479/AT-ISTA-20551}, year = {2025}, } @phdthesis{19048, abstract = {Rotations are found in physics problems at all scales: from spatial motion of celestial bodies, to transitions between quantum states of atoms and molecules. Mathematically, they represent a fundamental class of transformations and symmetries. Unlike spatial displacements, rotational transformations in three-dimensional space are non-commutative: the result of applying a sequence of rotations depends on the order of these operations. This feature makes the emergent physics that involves rotations rather intricate, but instrumental for studies of highly-interconnected many-body systems. In the presence of an environment, rotational properties of an object change, due to the interaction with particles of the environment. Owing to the complexity of this interaction, it can be engineered to exhibit certain properties of interest. In this Thesis, we examine several scenarios of how the rotational behavior of an impurity can be modified by interactions with its environment.}, author = {Maslov, Mikhail}, issn = {2663-337X}, pages = {86}, publisher = {Institute of Science and Technology Austria}, title = {{Emergent physics of rotating quantum impurities in many-body environments}}, doi = {10.15479/at:ista:19048}, year = {2025}, } @inproceedings{19518, abstract = {The rising footprint of machine learning has led to a focus on imposing model sparsity as a means of reducing computational and memory costs. For deep neural networks (DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics inspired by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al., 1989, Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature information to make better pruning decisions. Yet, these results still lack a solid theoretical understanding, and it is unclear whether they can be improved by leveraging connections to the wealth of work on sparse recovery algorithms. In this paper, we draw new connections between these two areas and present new sparse recovery algorithms inspired by the OBS framework that comes with theoretical guarantees under reasonable assumptions and have strong practical performance. Specifically, our work starts from the observation that we can leverage curvature information in OBS-like fashion upon the projection step of classic iterative sparse recovery algorithms such as IHT. We show for the first time that this leads both to improved convergence bounds under standard assumptions. Furthermore, we present extensions of this approach to the practical task of obtaining accurate sparse DNNs, and validate it experimentally at scale for Transformer-based models on vision and language tasks.}, author = {Wu, Diyuan and Modoranu, Ionut-Vlad and Safaryan, Mher and Kuznedelev, Denis and Alistarh, Dan-Adrian}, booktitle = {38th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {Vancouver, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information}}, volume = {37}, year = {2024}, } @inproceedings{18975, abstract = {Leveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression before it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99% sparsity without accuracy loss, effectively removing the memory overhead of fullmatrix preconditioners such as GGT and M-FAC.}, author = {Modoranu, Ionut-Vlad and Kalinov, Aleksei and Kurtic, Eldar and Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {35910--35933}, publisher = {ML Research Press}, title = {{Error feedback can accurately compress preconditioners}}, volume = {235}, year = {2024}, } @inproceedings{19510, abstract = {We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called MICROADAM that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this by compressing the gradient information before it is fed into the optimizer state, thereby reducing its memory footprint significantly. We control the resulting compression error via a novel instance of the classical error feedback mechanism from distributed optimization [Seide et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which the error correction information is itself compressed to allow for practical memory gains. We prove that the resulting approach maintains theoretical convergence guarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MICROADAM can be implemented efficiently on GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence competitive to that of the uncompressed Adam baseline, with lower memory usage and similar running time. Our code is available at https://github.com/IST-DASLab/MicroAdam.}, author = {Modoranu, Ionut-Vlad and Safaryan, Mher and Malinovsky, Grigory and Kurtic, Eldar and Robert, Thomas and Richtárik, Peter and Alistarh, Dan-Adrian}, booktitle = {38th Conference on Neural Information Processing Systems}, issn = {1049-5258}, publisher = {Neural Information Processing Systems Foundation}, title = {{MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence}}, doi = {10.52202/079017-0001}, volume = {37}, year = {2024}, } @phdthesis{18531, abstract = {Sex chromosomes and autosomes exhibit very different evolutionary dynamics. The Y chromosome usually degenerates, leaving many X-linked loci hemizygous in males. Since recessive X-linked mutations are always exposed to selection in males, selection is more efficient on the X chromosome than on autosomes on recessive mutations, leading to faster adaptation on the X chromosome than other genomic regions, if beneficial mutations are on average recessive (known as the Faster-X effect). In the presence of the functional, but non-recombining gametolog on the Y (as is often the case in young non-recombining regions), recessive mutations are sheltered from selection on the X chromosome. We model this scenario and show that the efficiency of selection is reduced on diploid X loci due to sheltering by the Y chromosome. Reduced efficiency of selection leads to slower adaptation and increased accumulation of deleterious mutations (Slower-X effect). We extended this model to explore the effect of sex-specific selection on degeneration of sex chromosomes, showing theoretically that male-limited genes degenerate on the X chromosome and female-biased genes degenerate on the Y chromosome. This prediction depends on the effective population size and the mutation rate, explaining the variety of sex chromosome degeneration patterns observed in nature. To test for direct evidence of a Slower-X (or Slower-Z) effect, we analyzed the ZW sex chromosomes of the flatworm Schistosoma japonicum, which have a very young non-recombining region with non-degenerated W. Diploid Z-linked genes have higher ratios of non-synonymous to synonymous polymorphisms than autosomal genes, supporting reduced efficiency of selection on the diploid Z region. These results provide evidence of sheltering by the W chromosome, a mechanism that could contribute to Z (X) chromosome degeneration, and illustrate contrasting evolutionary patterns in old and young sex chromosome regions. In addition, genes with sexspecific patterns of expression show opposite patterns of selection in the young (diploid) and old (hemizygous) Z, showing the complex manner in which sex-specific selection shapes the evolutionary patterns of sex chromosomes. }, author = {Mrnjavac, Andrea}, issn = {2663-337X}, keywords = {Sex chromosomes, evolution, selection, sheltering}, pages = {181}, publisher = {Institute of Science and Technology Austria}, title = {{Early stages of sex chromosome evolution}}, doi = {10.15479/at:ista:18531}, year = {2024}, } @phdthesis{12716, abstract = {The process of detecting and evaluating sensory information to guide behaviour is termed perceptual decision-making (PDM), and is critical for the ability of an organism to interact with its external world. Individuals with autism, a neurodevelopmental condition primarily characterised by social and communication difficulties, frequently exhibit altered sensory processing and PDM difficulties are widely reported. Recent technological advancements have pushed forward our understanding of the genetic changes accompanying this condition, however our understanding of how these mutations affect the function of specific neuronal circuits and bring about the corresponding behavioural changes remains limited. Here, we use an innate PDM task, the looming avoidance response (LAR) paradigm, to identify a convergent behavioural abnormality across three molecularly distinct genetic mouse models of autism (Cul3, Setd5 and Ptchd1). Although mutant mice can rapidly detect threatening visual stimuli, their responses are consistently delayed, requiring longer to initiate an appropriate response than their wild-type siblings. Mutant animals show abnormal adaptation in both their stimulus- evoked escape responses and exploratory dynamics following repeated stimulus presentations. Similarly delayed behavioural responses are observed in wild-type animals when faced with more ambiguous threats, suggesting the mutant phenotype could arise from a dysfunction in the flexible control of this PDM process. Our knowledge of the core neuronal circuitry mediating the LAR facilitated a detailed dissection of the neuronal mechanisms underlying the behavioural impairment. In vivo extracellular recording revealed that visual responses were unaffected within a key brain region for the rapid processing of visual threats, the superior colliculus (SC), indicating that the behavioural delay was unlikely to originate from sensory impairments. Delayed behavioural responses were recapitulated in the Setd5 model following optogenetic stimulation of the excitatory output neurons of the SC, which are known to mediate escape initiation through the activation of cells in the underlying dorsal periaqueductal grey (dPAG). In vitro patch-clamp recordings of dPAG cells uncovered a stark hypoexcitability phenotype in two out of the three genetic models investigated (Setd5 and Ptchd1), that in Setd5, is mediated by the misregulation of voltage-gated potassium channels. Overall, our results show that the ability to use visual information to drive efficient escape responses is impaired in three diverse genetic mouse models of autism and that, in one of the models studied, this behavioural delay likely originates from differences in the intrinsic excitability of a key subcortical node, the dPAG. Furthermore, this work showcases the use of an innate behavioural paradigm to mechanistically dissect PDM processes in autism.}, author = {Burnett, Laura}, issn = {2663-337X}, pages = {178}, publisher = {Institute of Science and Technology Austria}, title = {{To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism}}, doi = {10.15479/at:ista:12716}, year = {2023}, } @phdthesis{14641, abstract = {Mutation rates represent the net result of complex interactions among various cellular processes and can dramatically influence the evolutionary fate of microbial populations. However, many popular techniques used to study mutations are subject to the confounding effects of heredity and the subtleties of adaptation to selection, all of which make it difficult to observe any dynamic responses of mutation rates to fitness challenges. Furthermore, in spite of the ubiquity of quorum sensing systems across the bacterial domain and relevance for many physiological behaviors, the effects of such mechanisms on mutation rate and adaptation remain poorly understood. In the following work, I present the development of a microfluidic droplet-based method to measure single base-pair mutation rates in growing populations of the bacterium Escherichia coli. I use this method to observe a stress-induced increase in mutation rate that is mediated by luxS, a highly conserved bacterial quorum sensing component. I also show that the aforementioned increase in mutation rate, and its associated control by luxS, corresponds to a higher degree of adaptability under competitive environments.}, author = {Hennessey-Wesen, Mike}, issn = {2663-337X}, keywords = {microfluidics, miceobiology, mutations, quorum sensing}, pages = {104}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive mutation in E. coli modulated by luxS}}, doi = {10.15479/at:ista:14641}, year = {2023}, } @phdthesis{9418, abstract = {Deep learning is best known for its empirical success across a wide range of applications spanning computer vision, natural language processing and speech. Of equal significance, though perhaps less known, are its ramifications for learning theory: deep networks have been observed to perform surprisingly well in the high-capacity regime, aka the overfitting or underspecified regime. Classically, this regime on the far right of the bias-variance curve is associated with poor generalisation; however, recent experiments with deep networks challenge this view. This thesis is devoted to investigating various aspects of underspecification in deep learning. First, we argue that deep learning models are underspecified on two levels: a) any given training dataset can be fit by many different functions, and b) any given function can be expressed by many different parameter configurations. We refer to the second kind of underspecification as parameterisation redundancy and we precisely characterise its extent. Second, we characterise the implicit criteria (the inductive bias) that guide learning in the underspecified regime. Specifically, we consider a nonlinear but tractable classification setting, and show that given the choice, neural networks learn classifiers with a large margin. Third, we consider learning scenarios where the inductive bias is not by itself sufficient to deal with underspecification. We then study different ways of ‘tightening the specification’: i) In the setting of representation learning with variational autoencoders, we propose a hand- crafted regulariser based on mutual information. ii) In the setting of binary classification, we consider soft-label (real-valued) supervision. We derive a generalisation bound for linear networks supervised in this way and verify that soft labels facilitate fast learning. Finally, we explore an application of soft-label supervision to the training of multi-exit models.}, author = {Bui Thi Mai, Phuong}, issn = {2663-337X}, pages = {125}, publisher = {Institute of Science and Technology Austria}, title = {{Underspecification in deep learning}}, doi = {10.15479/AT:ISTA:9418}, year = {2021}, } @phdthesis{8032, abstract = {Algorithms in computational 3-manifold topology typically take a triangulation as an input and return topological information about the underlying 3-manifold. However, extracting the desired information from a triangulation (e.g., evaluating an invariant) is often computationally very expensive. In recent years this complexity barrier has been successfully tackled in some cases by importing ideas from the theory of parameterized algorithms into the realm of 3-manifolds. Various computationally hard problems were shown to be efficiently solvable for input triangulations that are sufficiently “tree-like.” In this thesis we focus on the key combinatorial parameter in the above context: we consider the treewidth of a compact, orientable 3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered triangulations, we establish quantitative relations between the treewidth and classical topological invariants of a 3-manifold. In particular, among other results, we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold is always within a constant factor of its Heegaard genus.}, author = {Huszár, Kristóf}, isbn = {978-3-99078-006-0}, issn = {2663-337X}, pages = {xviii+120}, publisher = {Institute of Science and Technology Austria}, title = {{Combinatorial width parameters for 3-dimensional manifolds}}, doi = {10.15479/AT:ISTA:8032}, year = {2020}, } @phdthesis{8390, abstract = {Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction for tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually dissimilar domains. }, author = {Royer, Amélie}, isbn = {978-3-99078-007-7}, issn = {2663-337X}, pages = {197}, publisher = {Institute of Science and Technology Austria}, title = {{Leveraging structure in Computer Vision tasks for flexible Deep Learning models}}, doi = {10.15479/AT:ISTA:8390}, year = {2020}, } @phdthesis{8983, abstract = {Metabolic adaptation is a critical feature of migrating cells. It tunes the metabolic programs of migrating cells to allow them to efficiently exert their crucial roles in development, inflammatory responses and tumor metastasis. Cell migration through physically challenging contexts requires energy. However, how the metabolic reprogramming that underlies in vivo cell invasion is controlled is still unanswered. In my PhD project, I identify a novel conserved metabolic shift in Drosophila melanogaster immune cells that by modulating their bioenergetic potential controls developmentally programmed tissue invasion. We show that this regulation requires a novel conserved nuclear protein, named Atossa. Atossa enhances the transcription of a set of proteins, including an RNA helicase Porthos and two metabolic enzymes, each of which increases the tissue invasion of leading Drosophila macrophages and can rescue the atossa mutant phenotype. Porthos selectively regulates the translational efficiency of a subset of mRNAs containing a 5’-UTR cis-regulatory TOP-like sequence. These 5’TOPL mRNA targets encode mitochondrial-related proteins, including subunits of mitochondrial oxidative phosphorylation (OXPHOS) components III and V and other metabolic-related proteins. Porthos powers up mitochondrial OXPHOS to engender a sufficient ATP supply, which is required for tissue invasion of leading macrophages. Atossa’s two vertebrate orthologs rescue the invasion defect. In my PhD project, I elucidate that Atossa displays a conserved developmental metabolic control to modulate metabolic capacities and the cellular energy state, through altered transcription and translation, to aid the tissue infiltration of leading cells into energy demanding barriers.}, author = {Emtenani, Shamsi}, issn = {2663-337X}, pages = {141}, publisher = {Institute of Science and Technology Austria}, title = {{Metabolic regulation of Drosophila macrophage tissue invasion}}, doi = {10.15479/AT:ISTA:8983}, year = {2020}, } @article{6755, abstract = {Differentiated sex chromosomes are accompanied by a difference in gene dose between X/Z-specific and autosomal genes. At the transcriptomic level, these sex-linked genes can lead to expression imbalance, or gene dosage can be compensated by epigenetic mechanisms and results into expression level equalization. Schistosoma mansoni has been previously described as a ZW species (i.e., female heterogamety, in opposition to XY male heterogametic species) with a partial dosage compensation, but underlying mechanisms are still unexplored. Here, we combine transcriptomic (RNA-Seq) and epigenetic data (ChIP-Seq against H3K4me3, H3K27me3,andH4K20me1histonemarks) in free larval cercariae and intravertebrate parasitic stages. For the first time, we describe differences in dosage compensation status in ZW females, depending on the parasitic status: free cercariae display global dosage compensation, whereas intravertebrate stages show a partial dosage compensation. We also highlight regional differences of gene expression along the Z chromosome in cercariae, but not in the intravertebrate stages. Finally, we feature a consistent permissive chromatin landscape of the Z chromosome in both sexes and stages. We argue that dosage compensation in schistosomes is characterized by chromatin remodeling mechanisms in the Z-specific region.}, author = {Picard, Marion A L and Vicoso, Beatriz and Roquis, David and Bulla, Ingo and Augusto, Ronaldo C. and Arancibia, Nathalie and Grunau, Christoph and Boissier, Jérôme and Cosseau, Céline}, issn = {1759-6653}, journal = {Genome biology and evolution}, number = {7}, pages = {1909--1922}, publisher = {Oxford University Press}, title = {{Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome}}, doi = {10.1093/gbe/evz133}, volume = {11}, year = {2019}, } @article{131, abstract = {XY systems usually show chromosome-wide compensation of X-linked genes, while in many ZW systems, compensation is restricted to a minority of dosage-sensitive genes. Why such differences arose is still unclear. Here, we combine comparative genomics, transcriptomics and proteomics to obtain a complete overview of the evolution of gene dosage on the Z-chromosome of Schistosoma parasites. We compare the Z-chromosome gene content of African (Schistosoma mansoni and S. haematobium) and Asian (S. japonicum) schistosomes and describe lineage-specific evolutionary strata. We use these to assess gene expression evolution following sex-linkage. The resulting patterns suggest a reduction in expression of Z-linked genes in females, combined with upregulation of the Z in both sexes, in line with the first step of Ohno’s classic model of dosage compensation evolution. Quantitative proteomics suggest that post-transcriptional mechanisms do not play a major role in balancing the expression of Z-linked genes. }, author = {Picard, Marion A and Cosseau, Celine and Ferré, Sabrina and Quack, Thomas and Grevelding, Christoph and Couté, Yohann and Vicoso, Beatriz}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Evolution of gene dosage on the Z-chromosome of schistosome parasites}}, doi = {10.7554/eLife.35684}, volume = {7}, year = {2018}, }