Pixinwav: Residual steganography for hiding pixels in audio

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual steganography setup we propose allows an encoding of the hidden image that is independent from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline—which can be later hidden, in a residual fashion, into any audio signal.Work partially supported by the European Union through the Erasmus+ student mobility program, Science Foundation Ireland (SFI) under grant numbers SFI/15/SIRG/3283 and SFI/12/RC/2289 P2, and the Spanish Research Agency (AEI) under project PID2020117142GB-I00 of the call MCIN/ AEI /10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Unsupervised learning with applications in genomics

Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression. In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of Single Nucleotide Polymorphisms (SNPs) as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression. Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multidimensional representation and as classification probabilities providing flexible and interpretable population descriptors. We train our VAE method with several worldwide whole genome datasets from both humans and canids, and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.Autocodificadors variacionals profunds per a la genètica de poblacions: aplicacions en classificació, imputació, reducció de dimensionalitat i innovadora compressió de dades sense pèrdues. En aquest estudi mostrem la potència dels autocodificadors variacionals (VAEs) per a una varietat de tasques relacionades amb la interpretació i compressió de dades genòmiques. El marc no supervisat permet detectar i aprendre l'estructura granular de les poblacions i inferir nous factors latents informatius, obrint la porta a aplicacions en reducció de dimensionalitat, simulació de dades, classificació d'ascendència, imputació i compressió de dades genòmiques sense pèrdues. L'espai latent dels VAEs és capaç de capturar i representar, per a composició genètica semblant, clústers Gaussians clarament diferenciats amb tot detall, utilitzant un nombre de polimorfismes de nucleòtids simples (SNPs) relativament petit com a entrada. A més, les seqüències poden ser descompostes en representacions latents i errors de reconstrucció (residuals) oferint una representació dispersa que permet una compressió eficient sense pèrdues. Identificar clústers genètics pot ser important a l'hora de fer estudis d'associació del genoma complet i ofereix una alternativa per a les etiquetes ètniques autoassignades, que s'han assignat culturalment i varien respecte la localització i l'individu. Antany, s'ha estudiat una varietat de mètodes no supervisats de reducció de dimensionalitat per a aquestes aplicacions, incloent-hi PCA, MDS, t-SNE i UMAP. El VAE proposat pot representar l'estructura de les poblacions com a una representació multidimensional contínua normalment distribuïda i com a probabilitats de classificació oferint uns descriptors de població flexibles i interpretables. Entrenem el nostre VAE amb diversos datasets de genomes complets mundials, tant d'humans com de cànids, i avaluem el rendiment en les diferents aplicacions proposades amb xarxes neuronals amb i sense condicionament d'ascendència. Els nostres experiments mostren que diferents grups d'ascendència tenen ràtios de compressió i precisió en classificació significativament diferenciats. Addicionalment, analitzem l'entropia de les dades de SNP, observant el seu efecte en la compressió per a les poblacions i connectem aquests patrons amb migracions històriques i relacions ancestrals

Generative moment matching networks for genotype simulation

The generation of synthetic genomic sequences using neural networks has potential to ameliorate privacy and data sharing concerns and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively assess the quality of the simulated sequences. © 2022 IEEE.Peer ReviewedPostprint (published version

Maestro: A Gamified Platform for Teaching AI Robustness

Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides "goal-based scenarios" where college students are exposed to challenging life-inspired assignments in a "competitive programming" environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly college courses in AI. According to the results, students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity, and maestry in robust AI. Moreover, the leaderboard, our key gamification element in Maestro, has effectively contributed to students' engagement and learning. Results also indicate that Maestro can be effectively adapted to any course length and depth without losing its educational quality

Mitochondrial genome acquisition restores respiratory function and tumorigenic potential of cancer cells without mitochondrial DNA

We report that tumor cells devoid of their mitochondrial genome (mtDNA) show delayed tumor growth and that tumor formation is associated with acquisition of mtDNA from host cells. This leads to partial recovery of mitochondrial function in cells derived from primary tumors grown from cells without mtDNA and a shorter lag in tumor growth. Cell lines from circulating tumor cells showed further recovery of mitochondrial respiration and an intermediate lag to tumor growth, while cells from lung metastases exhibited full restoration of respiratory function and no lag in tumor growth. Stepwise assembly of mitochondrial respiratory supercomplexes was correlated with acquisition of respiratory function. Our findings indicate horizontal transfer of mtDNA from host cells in the tumor microenvironment to tumor cells with compromised respiratory function to re-establish respiration and tumor-initiating efficacy. These results suggest a novel pathophysiological process for overcoming mtDNA damage and support the notion of high plasticity of malignant cells

Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints

Cell growth and survival depend on a delicate balance between energy production and synthesis of metabolites. Here, we provide evidence that an alternative mitochondrial complex II (CII) assembly, designated as CII, serves as a checkpoint for metabolite biosynthesis under bioenergetic stress, with cells suppressing their energy utilization by modulating DNA synthesis and cell cycle progression. Depletion of CII leads to an imbalance in energy utilization and metabolite synthesis, as evidenced by recovery of the de novo pyrimidine pathway and unlocking cell cycle arrest from the S-phase. In vitro experiments are further corroborated by analysis of paraganglioma tissues from patients with sporadic, SDHA and SDHB mutations. These findings suggest that CII is a core complex inside mitochondria that provides homeostatic control of cellular metabolism depending on the availability of energy

Reactivation of Dihydroorotate Dehydrogenase-Driven Pyrimidine Biosynthesis Restores Tumor Growth of Respiration-Deficient Cancer Cells

Cancer cells without mitochondrial DNA (mtDNA) do not form tumors unless they reconstitute oxidative phosphorylation (OXPHOS) by mitochondria acquired from host stroma. To understand why functional respiration is crucial for tumorigenesis, we used time-resolved analysis of tumor formation by mtDNA-depleted cells and genetic manipulations of OXPHOS. We show that pyrimidine biosynthesis dependent on respiration-linked dihydroorotate dehydrogenase (DHODH) is required to overcome cell-cycle arrest, while mitochondrial ATP generation is dispensable for tumorigenesis. Latent DHODH in mtDNA-deficient cells is fully activated with restoration of complex III/IV activity and coenzyme Q redox-cycling after mitochondrial transfer, or by introduction of an alternative oxidase. Further, deletion of DHODH interferes with tumor formation in cells with fully functional OXPHOS, while disruption of mitochondrial ATP synthase has little effect. Our results show that DHODH-driven pyrimidine biosynthesis is an essential pathway linking respiration to tumorigenesis, pointing to inhibitors of DHODH as potential anti-cancer agents.status: publishe

Reactivation of Dihydroorotate Dehydrogenase-Driven Pyrimidine Biosynthesis Restores Tumor Growth of Respiration-Deficient Cancer Cells

Cancer cells without mitochondrial DNA (mtDNA) do not form tumors unless they reconstitute oxidative phosphorylation (OXPHOS) by mitochondria acquired from host stroma. To understand why functional respiration is crucial for tumorigenesis, we used time-resolved analysis of tumor formation by mtDNA-depleted cells and genetic manipulations of OXPHOS. We show that pyrimidine biosynthesis dependent on respiration-linked dihydroorotate dehydrogenase (DHODH) is required to overcome cell-cycle arrest, while mitochondrial ATP generation is dispensable for tumorigenesis. Latent DHODH in mtDNA-deficient cells is fully activated with restoration of complex III/IV activity and coenzyme Q redox-cycling after mitochondrial transfer, or by introduction of an alternative oxidase. Further, deletion of DHODH interferes with tumor formation in cells with fully functional OXPHOS, while disruption of mitochondrial ATP synthase has little effect. Our results show that DHODH-driven pyrimidine biosynthesis is an essential pathway linking respiration to tumorigenesis, pointing to inhibitors of DHODH as potential anti-cancer agents