### The TR Prize are monthly awards for the best new research.

#### 1. The reverse TCA cycle and reductive amino acid synthesis pathways contribute to electron balance in a Rhodospirillum rubrum Calvin cycle mutant

19 April 2019 | Biorxiv link | Write review

Purple nonsulfur bacteria (PNSB) use light for energy and organic substrates for carbon and electrons when growing photoheterotrophically. This lifestyle generates more reduced electron carriers than are required for biosynthesis. It is essential that this excess reducing power be oxidized for photoheterotrophic growth to occur. Diverse PNSB commonly rely on the CO2-fixing Calvin cycle to oxidize excess reducing power. Some PNSB additionally utilize H2 production or reduction of electron acceptors, such as dimethylsulfoxide, as alternative reductive pathways to the Calvin cycle. Rhodospirillum rubrum Calvin cycle mutants defy this trend by growing phototrophically on relatively oxidized substrates like malate and fumarate without H2 production or access to electron acceptors. How Rs. rubrum Calvin cycle mutants maintain electron balance under these conditions was unknown. Here, using 13C-tracer experiments and physiological assays, we found that Rs. rubrum Calvin cycle mutants use a reductive arm of the tricarboxylic acid cycle when growing phototrophically on malate and fumarate. The reductive synthesis of amino acids stemming from -ketoglutarate is also likely important for electron balance, as supplementing the growth medium with -ketoglutarate-derived amino acids prevented Rs. rubrum Calvin cycle mutant growth unless dimethylsulfoxide was provided as an electron acceptor. Fluxes estimated from 13C-tracer experiments also suggested the preferential use of a reductive isoleucine synthesis pathway when the Calvin cycle was genetically inactivated; however, this pathway was not essential for growth of a Calvin cycle mutant.

#### 2. Spontaneous formation of chaotic protrusions in a polymerizing active gel layer

19 April 2019 | Arxiv link | Write review

The actin cortex is a thin layer of actin filaments and myosin motors beneath the outer membrane of animal cells. It determines the cells' mechanical properties and forms important morphological structures. Physical descriptions of the cortex as a contractile active gel suggest that these structures can result from dynamic instabilities. However, in these analyses the cortex is described as a two-dimensional layer. Here, we show that the dynamics of the cortex is qualitatively different when gel fluxes in the direction perpendicular to the membrane are taken into account. In particular, an isotropic cortex is then stable for arbitrarily large active stresses. If lateral contractility exceeds vertical contractility, the system can either from protrusions with an apparently chaotic dynamics or a periodic static pattern of protrusions.

#### 3. Automated analysis of whole brain vasculature using machine learning

19 April 2019 | Biorxiv link | Write review

Tissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.

#### 4. Multimodal Cross-registration and Quantification of Metric Distortions in Whole Brain Histology of Marmoset using Diffeomorphic Mappings

19 April 2019 | Arxiv link | Write review

Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo brains due to the tissue processing, which will be important when computing properties such as local cell and process densities at the voxel level in creating reference brain maps. Further, existing approaches focus on registering uni-modal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research, it is necessary to cross-register multi-modal data sets including MRIs and multiple histological series that can help address individual variations in brain architecture. Here we present a computational approach for same-subject multimodal MRI guided reconstruction of a histological series, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during the different stages of histological processing of the brains using the Jacobian determinant of the diffeomorphic transformations involved. There are two major steps in the histology process with associated scale distortions (a) brain perfusion (b) histological sectioning and reassembly. By mapping the final image stacks to the ex-vivo post fixation MRI, we show that tape-transfer histology can be reassembled accurately into 3D volumes with a local scale change of 2.0 $\pm$ 0.4% per axis dimension. In contrast, the perfusion step, as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs, shows a larger local scale change of 6.9 $\pm$ 2.1% per axis dimension. This is the first systematic quantification of the local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species.

#### 5. Defective photosynthetic adaptation mechanism in winter restricts the introduction of overwintering plant to high latitudes

18 April 2019 | Biorxiv link | Write review

Because of the need for agriculture and landscaping, many overwintering evergreen and biennial species that maintain green leaves over winter were introduced to higher latitudes. The green leaves of introduced overwintering species have to withstand a harsher winter, especially lower temperature, than in their native region of origin. Although the responses and adaptability of photosynthetic apparatus to winter conditions in native overwintering species were widely studied, the experimental results on the introduced overwintering species are very limited. Here, the photosynthetic adaptability during winter was analyzed in two native overwintering species, pine (woody plants), winter wheat (herb), and two introduced overwintering species, bamboo (woody plants), lilyturf (herb). The native species exhibited higher capacity for photosynthetic CO2 fixation and lower susceptibility for photoinhibition than introduced species during winter. Photosynthesis related proteins, such as PsbA, PsaA, Rubisco and Lhcb1, were marginally affected in native species, but significantly degraded in introduced species during winter. More interestingly, the PSII photoinhibition was mainly caused by up-regulation of photoprotection mechanism, non-photochemical quenching, in native species, but by photodamage in introduced species. This study indicates that the growth and survival of introduced overwintering species is limited by their photosynthetic adaptability to the harsher winter conditions at high latitudes.

#### 6. Dynamics of a Mathematical Hematopoietic Stem-Cell Population Model

18 April 2019 | Arxiv link | Write review

We explore the bifurcations and dynamics of a scalar differential equation with a single constant delay which models the population of human hematopoietic stem cells in the bone marrow. One parameter continuation reveals that with a delay of just a few days, stable periodic dynamics can be generated of all periods from about one week up to one decade! The long period orbits seem to be generated by several mechanisms, one of which is a canard explosion, for which we approximate the dynamics near the slow manifold. Two-parameter continuation reveals parameter regions with even more exotic dynamics including quasi-periodic and phase-locked tori, and chaotic solutions. The panoply of dynamics that we find in the model demonstrates that instability in the stem cell dynamics could be sufficient to generate the rich behaviour seen in dynamic hematological diseases.

#### 7. Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium

18 April 2019 | Biorxiv link | Write review

Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and nonsmokers, we generated a comprehensive atlas of epithelial cell types and states, connected these into lineages, and defined cell-specific responses to smoking. Our analysis inferred multi-state lineages that develop into surface mucus secretory and ciliated cells and contrasted these to the unique lineage and specialization of submucosal gland (SMG) cells. Our analysis also suggests a lineage relationship between tuft, pulmonary neuroendocrine, and the newly discovered CFTR-rich ionocyte cells. Our smoking analysis found that all cell types, including protected stem and SMG populations, are affected by smoking, through both pan-epithelial smoking response networks and hundreds of cell type-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.

#### 8. Unexpected links reflect the noise in networks

18 April 2019 | Arxiv link | Write review

Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.

#### 9. Molecular Diversity and Network Complexity in Growing Protocells

17 April 2019 | Biorxiv link | Write review

A great variety of molecular components is encapsulated in cells. Each of these components is replicated for cell reproduction. To address an essential role of the huge diversity of cellular components, we study a model of protocells that convert resources into catalysts with the aid of a catalytic reaction network. As the resources are limited, it is shown that diversity in intracellular components is increased to allow the use of diverse resources for cellular growth. Scaling relation is demonstrated between resource abundances and molecular diversity. We then study how the molecule species diversify and complex catalytic reaction networks develop through the evolutionary course. It is shown that molecule species first appear, at some generations, as parasitic ones that do not contribute to replication of other molecules. Later, the species turn to be host species that support the replication of other species. With this successive increase of host species, a complex joint network evolves. The present study sheds new light on the origin of molecular diversity and complex reaction network at the primitive stage of a cell.

#### 10. Nonparametric estimation of multivariate distribution function for truncated and censored lifetime data

17 April 2019 | Arxiv link | Write review

A number of models for generating statistical data in various fields of insurance, including life insurance, pensions, and general insurance have been considered. It is shown that the insurance statistics data, as a rule, are truncated and censored, and often multivariate. We propose a non-parametric estimation of the distribution function for multivariate truncated-censored data in the form of a quasi-empirical distribution and a simple iterative algorithm for its construction. To check the accuracy of the proposed evaluation of the distribution function for truncated-censored data, simulation studies have been conducted, which showed its high efficiency. The proposed estimates have been tested for many years by the IAAC Group of Companies in the actuarial valuation of corporate social liabilities according to IAS 19 Employee Benefits. Apart from insurance, some results of the work can be used, for example in medicine, biology, demography, mathematical theory of reliability, etc.

#### 11. piRNA-guided co-transcriptional silencing coopts nuclear export factors

17 April 2019 | Biorxiv link | Write review

The PIWI-interacting RNA (piRNA) pathway is a small RNA-based immune system that controls the expression of transposons and maintains genome integrity in animal gonads. In Drosophila, piRNA-guided silencing is achieved, in part, via co-transcriptional repression of transposons by Piwi. This depends on Panoramix (Panx); however, precisely how an RNA binding event silences transcription remains to be determined. Here we show that Nuclear Export Factor 2 (Nxf2) and its co-factor, Nxt1, form a complex with Panx and are required for co-transcriptional silencing of transposons in somatic and germline cells of the ovary. Tethering of Nxf2 or Nxt1 to RNA results in silencing of target loci and the concomitant accumulation of repressive chromatin marks. Nxf2 and Panx proteins are mutually required for proper localization and stability. We mapped the protein domains crucial for the Nxf2/Panx complex formation and show that the amino-terminal portion of Panx is sufficient to induce transcriptional silencing.

#### 12. Mathematical Modeling and Stability of Predator-Prey Systems

17 April 2019 | Arxiv link | Write review

This work investigated the stability and asymptotic behavior of some Lotka Volterra type models. We used the Liapunov method which consists in analyzing the stability of systems of ordinary differential equations (ODEs) around the equilibrium when they submitted to perturbations in the initial conditions

#### 13. Optimized but not maximized cue integration for 3D visual perception

16 April 2019 | Biorxiv link | Write review

Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-posed problem. Despite this, our 3D perception of the world based on 2D retinal images is seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D perception in humans, but it is unclear if this mechanism is conserved in non-human primates, and how the underlying neural architecture constrains 3D perception. Here we assess 3D perception in macaque monkeys using a surface orientation discrimination task. We find that perception is generally accurate, but precision depends on the spatial pose of the surface and available cues. The results indicate that robust perception is achieved by dynamically reweighting the integration of stereoscopic and perspective cues according to their pose-dependent reliabilities. They further suggest that 3D perception is influenced by a prior for the 3D orientation statistics of natural scenes. We compare the data to simulations based on the responses of 3D orientation selective neurons. The results are explained by a model in which two independent neuronal populations representing stereoscopic and perspective cues (with perspective signals from the two eyes combined using nonlinear canonical computations) are optimally integrated through linear summation. Perception of combined-cue stimuli is optimal given this architecture. However, an alternative architecture in which stereoscopic cues and perspective cues detected by each eye are represented by three independent populations yields two times greater precision than observed. This implies that, due to canonical computations, cue integration for 3D perception is optimized but not maximized.

#### 14. On the parameters affecting dual-target-function evaluation of single-particle selection from cryo-electron micrographs

16 April 2019 | Arxiv link | Write review

In the analysis of frozen hydrated biomolecules by single-particle cryo-electron microscopy, template-based particle picking by a target function called fast local correlation (FLC) allows a large number of particle images to be automatically picked from micrographs. A second, independent target function based on maximum likelihood (ML) can be used to align the images and verify the presence of signal in the picked particles. Although the paradigm of this dual-target-function (DTF) evaluation of single-particle selection has been practiced in recent years, it remains unclear how the performance of this DTF approach is affected by the signal-to-noise ratio of the images and by the choice of references for FLC and ML. Here we examine this problem through a systematic study of simulated data, followed by experimental substantiation. We quantitatively pinpoint the critical signal-to-noise ratio (SNR), at which the DTF approach starts losing its ability to select and verify particles from cryo-EM micrographs. A Gaussian model is shown to be as effective in picking particles as a single projection view of the imaged molecule in the tested cases. For both simulated micrographs and real cryo-EM data of the 173-kDa glucose isomerase complex, we found that the use of a Gaussian model to initialize the target functions suppressed the detrimental effect of reference bias in template-based particle selection. Given a sufficient signal-to-noise ratio in the images and the appropriate choice of references, the DTF approach can expedite the automated assembly of single-particle data sets.

#### 15. Deleterious in late life mitochondrial alleles and aging: secrets of Japanese centenarians

16 April 2019 | Biorxiv link | Write review

Aging is associated with accumulation of somatic mutations. This process is especially pronounced in mitochondrial genomes of postmitotic cells, which accumulate large-scale somatic mitochondrial deletions with time, leading to neurodegeneration, muscular dystrophy and aging. Slowing down the rate of origin of these somatic deletions may benefit human lifespan and healthy aging. The main factors determining breakpoints of somatic mitochondrial deletions are direct nucleotide repeats, which might be considered as Deleterious In Late Life (DILL) alleles. Correspondingly, the decreased amount of these DILL alleles might lead to low production of somatic deletions and increased lifespan. Intriguingly, in the Japanese D4a haplogroup, which is famous for an excess of centenarians and supercentenarians, we found that the longest direct repeat ("common repeat") in the human mitochondrial genome has been disrupted by a point synonymous mutation. Thus we hypothesize that the disruption of the common repeat annuls common deletion (which is the most frequent among all somatic deletions) and at least partially may contribute to the extreme longevity of the D4a Japanese haplogroup. Here, to better understand the mitochondrial components of longevity and potential causative links between repeats, deletions and longevity we discuss molecular, population and evolutionary factors affecting dynamics of mitochondrial direct repeats.

#### 16. Dual-target function validation of single-particle selection from low-contrast cryo-electron micrographs

16 April 2019 | Arxiv link | Write review

Weak-signal detection and single-particle selection from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) presents a practical challenge. Cryo-EM image contrast degrades as the size of biomolecules of structural interest decreases. When the image contrast falls into a range where the location or presence of single particles becomes ambiguous, a need arises for objective computational approaches to detect weak signal and to select and verify particles from these low-contrast micrographs. Here we propose an objective validation scheme for low-contrast particle selection using a combination of two different target functions. In an implementation of this dual-target function (DTF) validation, a first target function of fast local correlation was used to select particles through template matching, followed by signal validation through a second target function of maximum likelihood. By a systematic study of simulated data, we found that such an implementation of DTF validation is capable of selecting and verifying particles from cryo-EM micrographs with a signal-to-noise ratio as low as 0.002. Importantly, we demonstrated that DTF validation can robustly evade over-fitting or reference bias from the particle-picking template, allowing true signal to emerge from amidst heavy noise in an objective fashion. The DTF approach allows efficient assembly of a large number of single-particle cryo-EM images of smaller biomolecules or specimens containing contrast-degrading agents like detergents in a semi-automatic manner.

#### 17. Robust Reconstruction of CRISPR and Tumor Lineage Using Depth Metrics

15 April 2019 | Biorxiv link | Write review

Lineage reconstruction using CRISPR edited barcodes are becoming wide-spread and methods robust against noise are in need. Neighbor-Joining (NJ) algorithm is a robust distance based algorithm extensively used in phylogeny field. NJ is also used for CRISPR-encoded-lineage (CEL) reconstruction with proper re-rooting since NJ is un-rooted algorithm. However, we found NJ works without re-rooting for reconstructing CEL when the lineage contains multiple trees but not for a single tree. Examining why this is the case leads to the idea of depth metrics. The notion of depth metrics also naturally explains why Russell-Rao metric, previously found best metric for CEL reconstruction, works well. Furthermore, based on the probabilistic model of CEL, we constructed a new metric that performs better than Russell-Rao metric. We also propose inferring ancestral code during reconstruction instead of using a linkage method. These, together with Nearest-Neighbor-Interchange resulted in a new robust method for reconstructing CEL or tumor-cell-lineages which share same assumptions as CEL.

#### 18. Fixation in Fluctuating Populations

15 April 2019 | Arxiv link | Write review

We investigate the dynamics of the voter model in which the population itself changes endogenously via the birth-death process. There are two species of voters, labeled A and B, and the population of each species can grow or shrink by the birth-death process at equal rates $b$. Individuals of opposite species also undergo voter model dynamics in which an AB pair can equiprobably become AA or BB with rate $v$---neutral evolution. In the limit $b/v\to\infty$, the distribution of consensus times varies as $t^{-3}$ and the probability that the population size equals $n$ at the moment of consensus varies as $n^{-3}$. As the birth/death rate $b$ is increased, fixation occurs more more quickly; that is, population fluctuations promote consensus.

#### 19. Identification of novel key biomarkers in Simpson-Golabi-Behmel Syndrome: Evidence from bioinformatics analysis

15 April 2019 | Biorxiv link | Write review

Background: The Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is a rare inherited X-linked condition characterized by pre- and postnatal overgrowth. The aim of the present study is to identify functional non-synonymous SNPs of GPC3 gene using various in silico approaches. These SNPs are supposed to have a direct effect on protein stability through conformation changes. Material and methods: The SNPs were retrieved from the Single Nucleotide Polymorphism database (dbSNP) and further used to investigate a damaging effect using SIFT, PolyPhen, PROVEAN, SNAP2, SNPs&GO, PHD-SNP and P-mut, While we used I-mutant and MUPro to study the effect of SNPs on GPC3 protein structure. The 3D structure of human GPC3 protein is not available in the Protein Data Bank, so we used RaptorX to generate a 3D structural model for wild-type GPC3 to visualize the amino acids changes by UCSF Chimera. For biophysical validation we used project HOPE. Lastly we run conservational analysis by BioEdit and Consurf web server respectively. Results: our results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are found to be the most deleterious which effect on the GPC3 structure and function. Conclusion: This present study could provide a novel insight into the molecular basis of overgrowth Syndrome.

#### 20. Learning to Design RNA

15 April 2019 | Arxiv link | Write review

Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation. Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints. Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure. By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks. Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process. Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance. In an ablation study, we analyze the importance of our method's different components.

#### 21. Amygdala controls saccade and gaze physically, motivationally, and socially

14 April 2019 | Biorxiv link | Write review

The amygdala is uniquely sensitive to emotional events. However, it is not understood whether and how the amygdala uses such emotional signals to control behavior, especially eye movements. We therefore injected muscimol (GABAA agonist) into the central nucleus of amygdala (CeA) in monkeys. This unilateral temporary inactivation suppressed saccades to contralateral but not ipsilateral targets, resulting in longer latencies, hypometric amplitudes, and slower velocity. During free viewing of movies, gaze was distributed mostly in the ipsilateral hemifield. Moreover, CeA inactivation disrupted the tendency of gaze toward social interaction images, which were normally focused on continuously. Conversely, optogenetic stimulation of CeA facilitated saccades to the contralateral side. These findings suggest that CeA controls spatially selective gaze and attention in emotional contexts, and provide a new framework for understanding psychiatric disorders related to amygdala dysfunction.

#### 22. Microglia monitor and protect neuronal function via specialized somatic purinergic junctions

14 April 2019 | Biorxiv link | Write review

Microglia are the main immune cells in the brain with emerging roles in brain homeostasis and neurological diseases, while mechanisms underlying microglia-neuron communication remain elusive. Here, we identify a novel site of interaction between neuronal cell bodies and microglial processes in mouse and human brain. Somatic microglia-neuron junctions possess specialized nanoarchitecture optimized for purinergic signaling. Activity of neuronal mitochondria is linked with microglial junction formation, which is rapidly induced in response to neuronal activation and blocked by inhibition of P2Y12-receptors (P2Y12R). Brain injury-induced changes at somatic junctions trigger P2Y12R-dependent microglial neuroprotection, regulating neuronal calcium load and functional connectivity. Collectively, our results suggest that microglial processes at these junctions are in ideal position to monitor and protect neuronal functions in both the healthy and injured brain.

#### 23. Daedalus and Gasz recruit Armitage to mitochondria, bringing piRNA precursors to the biogenesis machinery

13 April 2019 | Biorxiv link | Write review

The piRNA pathway is a small RNA-based immune system that silences mobile genetic elements in animal germlines. piRNA biogenesis requires a specialised machinery that converts long single-stranded precursors into small RNAs of ~25-nucleotides in length. This process involves factors that operate in two different subcellular compartments: the nuage/Yb-body and mitochondria. How these two sites communicate to achieve accurate substrate selection and efficient processing remains unclear. Here, we investigate a previously uncharacterized piRNA biogenesis factor, Daedalus (Daed), that is located on the outer mitochondrial membrane. Daed is essential for Zucchini-mediated piRNA production and for the correct localisation of the indispensable piRNA biogenesis factor, Armitage (Armi). We find that Gasz and Daed interact with each other and likely provide a mitochondrial "anchoring platform" to ensure that Armi is held in place, proximal to Zucchini, during piRNA processing. Our data suggest that Armi initially identifies piRNA precursors in nuage/Yb-bodies in a manner that depends upon Piwi and then moves to mitochondria to present precursors to the mitochondrial biogenesis machinery. These results represent a significant step in understanding a critical aspect of transposon silencing, namely how RNAs are chosen to instruct the piRNA machinery in the nature of its silencing targets.

#### 24. A generalized kinetic framework applied to whole-cell catalysis in biofilm flow reactors clarifies performance enhancements

13 April 2019 | Arxiv link | Write review

A common kinetic framework for studies of whole-cell catalysis is vital for understanding and optimizing bioflow reactors. In this work, we demonstrate the applicability of a flow-adapted version of Michaelis-Menten kinetics to a catalytic bacterial biofilm. A three-electrode microfluidic electrochemical flow cell measured increased turnover rates by as much as 50% from a Geobacter sulfurreducens biofilm as flow rate was varied. Based on parameters from the applied kinetic framework, flow-induced increases to turnover rate, catalytic efficiency and device reaction capacity could be linked to an increase in catalytic biomass. This study demonstrates that a standardized kinetic framework is critical for quantitative measurements of new living catalytic systems in flow cells and for benchmarking against well-studied catalytic systems such as enzymes.

#### 25. Gibbs Process Determines Survival and Reveals Contact-Inhibition Genes in Glioblastoma Multiforme

13 April 2019 | Biorxiv link | Write review

Tumor growth is a spatiotemporal birth-and-death process with loss of heterotypic contact-inhibition of locomotion (CIL) of tumor cells promoting invasion and metastasis. Therefore, representing tumor cells as two-dimensional points, we can expect the tumor tissues in histology slides to reflect realizations of spatial birth-and-death process which can be mathematically modeled to reveal molecular mechanisms of CIL, provided the mathematics models the inhibitory interactions. Gibbs process as an inhibitory point process is a natural choice since it is an equilibrium process of the spatial birth-and-death process. That is if the tumor cells maintain homotypic contact inhibition, the spatial distributions of tumor cells will result in Gibbs hard core process over long time scales. In order to verify if this is the case, we applied the Gibbs process to 411 TCGA Glioblastoma multiforme patient images. Our imaging dataset included all cases for which diagnostic slide images were available. The model revealed two clusters, one of which - the "Gibbs cluster," showed the convergence of the Gibbs process with significant survival difference. Further smoothing the discretized (and noisy) inhibition metric, for both increasing and randomized survival time, we found a significant association of the patients in the Gibbs cluster with increasing survival time. The mean inhibition metric also revealed the point at which the homotypic CIL establishes in tumor cells. Besides, RNAseq analysis between patients with loss of heterotypic CIL and intact homotypic CIL in the Gibbs cluster unveiled cell movement gene signatures and differences in Actin cytoskeleton and RhoA signaling pathways as key molecular alterations. These genes and pathways have established roles in CIL. Taken together, our integrated analysis of patient images and RNAseq data provides for the first time a mathematical basis for CIL in tumors, explains survival as well as uncovers the underlying molecular landscape for this key tumor invasion and metastatic phenomenon.

#### 26. Scanner Invariant Representations for Diffusion MRI Harmonization

13 April 2019 | Arxiv link | Write review

Pooled imaging data from multiple sources is subject to variation between the sources. Correcting for these biases has become incredibly important as the size of imaging studies increases and the multi-site case becomes more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to the underlying structure. To implement this, we use a machine learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. To evaluate our method, we use training data from the 2018 CDMRI Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method.

#### 27. TMS of V1 eliminates unconscious processing of chromatic stimuli

12 April 2019 | Biorxiv link | Write review

Some of the neurological patients with primary visual cortex (V1) lesions can guide their behavior based on stimuli presented to their blind visual field. One example of this phenomenon is the ability to discriminate colors in the absence of awareness. These so-called patients with blindsight must have a neural pathway that bypasses the V1, explaining their ability to unconsciously process stimuli. To test if similar pathways function in neurologically healthy individuals or if unconscious processing depends on the V1, we disturbed the visibility of a chromatic stimulus with metacontrast masking (Experiment 1) or transcranial magnetic stimulation (TMS) of the V1 (Experiment 2). We measured unconscious processing using the redundant target effect (RTE), which is the speeding up of reaction times in response to dual stimuli compared with one stimulus, when the task is to respond to any number of stimuli. An unconscious chromatic RTE was found when the visibility of the redundant chromatic stimulus was suppressed with a visual mask. When TMS was applied to the V1 to disturb the perception of the redundant chromatic stimulus, the RTE was eliminated. Based on our results and converging evidence from previous studies, we conclude that the unconscious processing of chromatic information depends on the V1 in neurologically healthy participants.

#### 28. The nature of the animacy organization in human ventral temporal cortex

12 April 2019 | Arxiv link | Write review

The principles underlying the animacy organization of the ventral temporal cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy continuum rather than a dichotomy. What drives this continuum? According to the visual categorization hypothesis, the continuum reflects the degree to which animals contain animate-diagnostic features. By contrast, the agency hypothesis posits that the continuum reflects the degree to which animals are perceived as social agents. Here, we tested both hypotheses with a stimulus set in which visual categorizability and agency were dissociated based on representations in convolutional neural networks and behavioral experiments. Using fMRI, we found that visual categorizability and agency explained independent components of the animacy continuum in VTC. Modeled together, they fully explained the animacy continuum. Further analyses revealed that the clusters explained by visual categorizability were localized posterior to the clusters explained by agency. These results provide evidence for multiple animacy continua in VTC.

#### 29. Hepatocellular carcinoma computational models identify key protein-complexes associated to tumor progression

12 April 2019 | Biorxiv link | Write review

Motivation: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to win insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this. The presented work proposes a workflow to generate novel computational predictions related to the state of expression or activity of biological molecules in the context of hepatocellular carcinoma (HCC) progression. Results: Our working base is a graph of 3,383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein-complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. Availability: Data and scripts are freely available at https://zenodo.org/record/2635752 and https://github.com/arnaudporet/stream .

#### 30. Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures

12 April 2019 | Arxiv link | Write review

We introduce Arbor, a performance portable library for simulation of large networks of multi-compartment neurons on HPC systems. Arbor is open source software, developed under the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, and NVIDIA GPUs. When coupled with low memory overheads, these optimizations make Arbor an order of magnitude faster than the most widely-used comparable simulation software. The single-node performance can be scaled out to run very large models at extreme scale with efficient weak scaling. HPC, GPU, neuroscience, neuron, software