Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproducible Tract Profiles (RTP2): from diffusion MRI acquisition to clinical practice and research

Diffusion MRI is a complex technique, where new discoveries and implementations occur at a fast pace. The expertise needed for data analyses and accurate and reproducible results is increasingly demanding and requires multidisciplinary collaborations. In the present work we introduce Reproducible Tract Profiles (RTP2): a set of flexible and automated methods to analyze anatomical MRI and diffusion weighted imaging (DWI) data for reproducible tractography. The tools read structural MRI data and process them through a succession of serialized containerized analyses. We describe the DWI algorithms used to identify white-matter tracts and their summary metrics, the flexible architecture of the platform, and the tools to programmatically access and control the computations. The combination of these three components provides an easy-to-use automatized tool developed and tested over 20 years, to obtain usable and reliable state-of-the-art diffusion metrics at the individual and group levels for basic research and clinical practice. Highlights Automated, flexible and reproducible protocol for white-matter tractography and tractometry. High computational (same data, different computations) and test-retest reproducibility (data from different sessions, different computations). Open-source code and publicly available containers.
Near telomere-to-telomere nuclear phased chromosomes of the dikaryotic wheat fungus Rhizoctonia cerealis

Rhizoctonia cerealis (Rce), which causes sharp eyespot, is one of the most destructive wheat pathogens. However, the genetic and molecular virulence mechanisms of Rce have not been elucidated. As a dikaryotic organism, the haplotype phasing of this fungus has not been completed so far. We applied a haplotype phasing algorithm to generate a high-quality near telomere-to-telomere nuclear-phased genome sequence of Rce strain R0301. Sixteen pairs of chromosomes were assigned to the A and B genomes with a total size of 83 Mb. Based on a dual-time course RNA-seq, 25308 genes were predicted. Genes for steroid biosynthesis and starch and sucrose metabolism were significantly enriched, together with many genes encoding carbohydrate-active enzymes (CAZymes) and secreted effector proteins, which should be involved in infection of wheat plants. Population genomic analysis of 31 isolates collected in China during the last forty years suggests that this population has not undergone substantial differentiation over time. Importance The finished genome reference is the basis of revealing pathogens’ biology base. Many efforts have been made to produce the chromosome-scale assembly of fungi. However, the reference of many pathogenic fungi is highly fragmented, which prevents the analysis of genome structure variation, evolution and import pathogenicity genes. Here, we assembly the only chromosome-scale haplotype-phased reference of dikaryotic fungus so far. This assembly achieves the gold standard based on many evaluation software, which indicates that the pipeline developed in this study can be applied to assemble references for other dikaryotic organisms. This work can also promote the research on the globe’s destructive wheat pathogens, sharp eyespot, caused by R. cerealis.
Characterizing brain imaging features associated with ADAS-Cog13 sub-scores with 3D convolutional neural networks

To date, few advanced machine learning models have been developed for investigating the associations between features from brain imaging and individual Alzheimer’s disease (AD) related cognitive functional changes. Additionally, how these associations differ among different imaging modalities is unclear. Here we investigated 3D convolutional neural network (CNN) models which were trained to predict sub-scores in 13-item Alzheimer’s Disease Assessment Scale - Cognitive Subscale (ADAS-Cog13) based on MRI and FDG-PET brain imaging data obtained from the ADNI database. We found that each key ADAS-Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Overall, sub-scores were strongly associated with structural changes of subcortical regions including amygdala, hippocampus, and putamen, and were associated with metabolic changes of cortical regions including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. Our findings provided insights into complex AD etiology. Our analytical pipeline can also be utilized to study other brain diseases.
Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation

Advances in functional brain imaging now allow sustained rapid 3D visualization of large numbers of neurons inside behaving animals. To decode circuit activity, imaged neurons must be individually segmented and tracked. This is particularly challenging when the brain itself moves and deforms inside a flexible body. The field has lacked general methods for solving this problem effectively. To address this need, we developed a method based on a convolutional neural network (CNN) Awith specific enhancements which we apply to freely moving Caenorhabditis elegans. For a traditional CNN to track neurons across images of a brain with different postures, the CNN must be trained with ground truth (GT) annotations of similar postures. When these postures are diverse, an adequate number of GT annotations can be prohibitively large to generate manually. We introduce ‘targeted augmentation’, a method to automatically synthesize reliable annotations from a few manual annotations. Our method effectively learns the internal deformations of the brain. The learned deformations are used to synthesize annotations for new postures by deforming the manual annotations of similar postures in GT images. The technique is germane to 3D images, which are generally more difficult to analyze than 2D images. The synthetic annotations, which are added to diversify training datasets, drastically reduce manual annotation and proofreading. Our method is effective both when neurons are represented as individual points or as 3D volumes. We provide a GUI that incorporates targeted augmentation in an end-to-end pipeline, from manual GT annotation of a few images to final proofreading of all images. We apply the method to simultaneously measure activity in the second-layer interneurons in C. elegans: RIA, RIB, and RIM, including the RIA neurite. We find that these neurons show rich behaviors, including switching entrainment on and off dynamically when the animal is exposed to periodic odor pulses.
Understandable Multifunctionality Measures Using Hill Numbers

In ecology, multifunctionality measures the simultaneous performance of multiple ecosystem functions. If species diversity describes the variety of species that together build the ecosystem, multifunctionality attempts to describe the variety of functions these species perform. A range of methods have been proposed to quantify multifunctionality, successively attempting to alleviate problems that have been identified with the previous methods. This has led to a proliferation of more-or-less closely related metrics which, however, lack an overarching theoretical framework. Here we borrow from the comprehensive framework of species diversity to derive a new metric of multifunctionality. Analogously to the effective number of species used to quantify species diversity, the metric we propose is influenced both by the number of functions as well as, crucially, the evenness of performance levels across functions. In addition, the effective multifunctionality also considers the average level at which the functions are performed. The result is a measure of the cumulative performance of the system were all functions provided equally. The framework allows for the inclusion of the correlation structure among functions, thus allowing it to account for non-independence between functions. We show that the average metric is a special case of the newly proposed metric when all functions are uncorrelated and performed at equal levels. We hope that by providing a new metric of multifunctionality anchored in the rigorous framework of species diversity based on effective numbers, we will overcome the considerable skepticism that the larger community of ecologists has built against indices of multifunctionality. We thereby hope to help popularize this important concept which, as biological diversity, describes a fundamental property of ecosystems and thus lies at the heart of ecology.
High-resolution species assignment of Anopheles mosquitoes using k-mer distances on targeted sequences

The ANOSPP amplicon panel is a genus-wide targeted sequencing panel to facilitate large-scale monitoring of Anopheles species diversity. Combining information from the 62 nuclear amplicons present in the ANOSPP panel allows for a more nuanced species assignment than single gene (e.g. COI) barcoding, which is desirable in the light of permeable species boundaries. Here, we present NNoVAE, a method using Nearest Neighbours (NN) and Variational Autoencoders (VAE), which we apply to k-mers resulting from the ANOSPP amplicon sequences in order to hierarchically assign species identity. The NN step assigns a sample to a species-group by comparing the k-mers arising from each haplotype’s amplicon sequence to a reference database. The VAE step is required to distinguish between closely related species, and also has sufficient resolution to reveal population structure within species. In tests on independent samples with over 80% amplicon coverage, NNoVAE correctly classifies to species level 98% of samples within the An. gambiae complex and 89% of samples outside the complex. We apply NNoVAE to over two thousand new samples from Burkina Faso and Gabon, identifying unexpected species in Gabon. NNoVAE presents an approach that may be of value to other targeted sequencing panels, and is a method that will be used to survey Anopheles species diversity and Plasmodium transmission patterns through space and time on a large scale, with plans to analyse half a million mosquitoes in the next five years.
Cavitation Induced Fracture of Intact Brain Tissue

Nonpenetrating traumatic brain injuries (TBI) are linked to cavitation. The structural organization of the brain makes it particularly susceptible to tears and fractures from these cavitation events, but limitations in existing characterization methods make it difficult to understand the relationship between fracture and cavitation in this tissue. More broadly, fracture energy is an important, yet often overlooked, mechanical property of all soft tissues. We combined needle-induced cavitation (NIC) with hydraulic fracture models to induce and quantify fracture in intact brains at precise locations. We report here the first measurements of the fracture energy of intact brain tissue that range from 1.5 to 8.9 J/m2, depending on the location in the brain and the model applied. We observed that fracture consistently occurs along interfaces between regions of brain tissue. These fractures along interfaces allow cavitation-related damage to propagate several millimeters away from the initial injury site. Quantifying the forces necessary to fracture brain and other soft tissues is critical for understanding how impact and blast waves damage tissue in vivo and has implications for the design of protective gear and tissue engineering. Significance Mild injuries associated with concussion and blast waves cause tearing of brain tissue, which leads to traumatic brain injury (TBI). TBI is a leading cause of death and disability among children and young adults in the U.S., with 1.5 million Americans reporting a TBI each year. We introduce a novel approach to visualize these tears in intact brain tissue, and report the energies associated with brain fracture. Quantifying the fracture energy of brain, as we have done here, is critical to understand the forces from injury that lead to TBI.
Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries

Current fragment-based drug design relies on the efficient exploration of chemical space though the use of structurally diverse libraries of small fragments. However, structurally dissimilar compounds can exploit the same interactions on a target, and thus be functionally similar. Using 3D structures of many fragments bound to multiple targets, we examined if there exists a better strategy for selecting fragments for screening libraries. We show that structurally diverse fragments can be described as functionally redundant, often making the same interactions. Ranking fragments by the number of novel interactions they made, we show that functionally diverse selections of fragments substantially increase the amount of information recovered for unseen targets compared to other methods of selection. Using these results, we design small functionally efficient libraries that are able to give significantly more information about new protein targets than similarly sized structurally diverse libraries. By covering more functional space (rather than chemical space), more diverse sets of drug leads can be generated, increasing the chances of fragment screens resulting in viable drug candidates.
Higher visual areas act like domain-general filters with strong selectivity and functional specialization

Neuroscientific studies rely heavily on a-priori hypotheses, which can bias results toward existing theories. Here, we use a hypothesis-neutral approach to study category selectivity in higher visual cortex. Using only stimulus images and their associated fMRI activity, we constrain randomly initialized neural networks to predict voxel activity. Despite no category-level supervision, the units in the trained networks act as detectors for semantic concepts like ‘faces’ or ‘words’, providing solid empirical support for categorical selectivity. Importantly, this selectivity is maintained when training the networks without images that contain the preferred category, strongly suggesting that selectivity is not domain-specific machinery, but sensitivity to generic patterns that characterize preferred categories. The ability of the models’ representations to transfer to perceptual tasks further reveals the functional role of their selective responses. Finally, our models show selectivity only for a limited number of categories, all previously identified, suggesting that the essential categories are already known. Teaser Models trained solely to predict fMRI activity from images reveal strong category selectivity in higher visual areas, even without exposure to these categories in training.
Are trapping data still suited for home range estimation? An analysis with various estimators, asymptotic models and data ordering procedures

Understanding the size of animals’ home ranges is vital for studies in ecology and conservation. Trapping datasets are an important source of information when targeting the biodiversity of an area, inconspicuous species, or high numbers of individuals in contrast to more expensive telemetry-based methods such as radio- or GPS-collaring. Currently, studies relying on trapping lack an evaluation of the performance of existing home range estimation procedures comparable to those developed for telemetry. Using animal movement simulations, we evaluate three variables reflecting the trade-offs faced by ecologists when designing a trapping study, 1) the number of observations obtained per individual, 2) the trap density and 3) the proportion of the home range area falling inside of the trapping grid. We compare the performance of five estimators on these conditions, four commonly used (AKDE, KDE, MCP, LoCoH) and a possible alternative for situations with low trap density or high number of observations (bicubic interpolation). We further test suggested benefits of using asymptotic models (Michaelis-Menten and monomolecular) to assess the total home range area when information obtained per individual is scarce, as this situation might be common in trapping datasets. In addition, we propose sorting the observations based on the distance between locations to improve the performance of asymptotic models’ estimates. Using the results of the different procedures we constructed a generalized additive model (GAM) that allows predicting the bias in home range size under the different scenarios investigated. Our results show that the proportion of the area covered by the trapping grid and the number of observations were the most important factors predicting the accuracy and reliability of the estimates. The use of asymptotic models helped obtaining an accurate estimation at lower sample sizes and this effect was further improved by distance-ordering. The autocorrelation informed KDE was the estimator performing best under most conditions evaluated. Nevertheless, bicubic interpolation can be an alternative under common trapping conditions with low density of traps and low area covered. We provide the current results to the constructed GAM as a prospective tool for ecologists planning a new study or with already collected datasets that aim at assessing the potential biases in their estimates. Reliable and accurate home range estimates using trapping data can optimize monetary costs of home range studies, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.
Spatio-temporal modeling reveals a layer of tunable control circuits for the distribution of cytokines in tissues

Cytokines are diffusible mediators of cell-cell communication among immune cells with critical regulatory functions for cell differentiation and proliferation. Previous studies have revealed considerable spatial inhomogeneities in the distribution of cytokine molecules in tissues, potentially shaping the efficacy and range of paracrine cytokine signals. How such cytokine gradients emerge and are controlled within cell populations is incompletely understood. In this work, we employed a spatial reaction-diffusion model to systematically investigate the formation and influence of spatial cytokine gradients. We found the fraction of cytokine secreting cells to be the main source of spatial inhomogeneity and subsequent activation. Positive feedback from local cytokine levels upon cytokine receptor expression leads to further increased spatial cytokine inhomogeneities. By exploring the effect of co-clustering cytokine secreting cells and cells with large amounts of receptor expression, as in the presence of regulatory T cells in the vicinity of antigen-presenting cells, we found that such constrained tissue architecture can have profound effects on the range of paracrine cytokine signals.
Case-control analysis of single-cell RNA-seq studies

Single-cell RNA-seq (scRNA-seq) assays are being increasingly utilized to investigate specific hypotheses in both basic biology and clinically-applied studies. The design of most such studies can be often reduced to a comparison between two or more groups of samples, such as disease cases and healthy controls, or treatment and placebo. Comparative analysis between groups of scRNA-seq samples brings additional statistical considerations, and currently there is a lack of tools to address this common scenario. Based on our experience with comparative designs, here we present a computational suite (Cacoa – case-control analysis) to carry out statistical tests, exploration, and visualization of scRNA-seq sample cohorts. Using multiple example datasets, we demonstrate how application of these techniques can provide additional insights, and avoid issues stemming from inter-individual variability, limited sample size, and high dimensionality of the data.
CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. The application of CNETML to real data from Barrett’s esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations.
Solid phase behavior in offshore natural gas liquefaction concepts

Three floating liquefaction concepts aiming at offshore natural gas recovery,Liquefied Natural Gas(LNG),Heavy Liquefied Gas(HLG)or another similar concept Pressurized Liquid Natural Gas(PLNG)and Liquefied Unprocessed Well Stream(LUWS)have gained interest in the past decade,control or prevention of solid formation is crucial for all these processes.In this work,the solubility of carbon dioxide and heavy hydrocarbons in the gas mixture at low temperature is simulated and compared with literature data.Using Soave-Redlich-Kwong(SRK)equation of state(EOS)for the fluid phase and a fugacity model for the solid phase,the simulation of carbon dioxide solid behavior agrees with experimental data.The phenomenon of carbon dioxide and benzene solids existing together in gas mixture is analyzed,the result shows the difference in dealing with the potential solid formation components in the offshore liquefaction concepts.
Cloning of nine glucocorticoid receptor isoforms from the slender African lungfish (Protopterus dolloi)

We wanted to clone the glucocorticoid receptor (GR) from slender African lungfish (Protopterus dolloi) for comparison to P. dolloi MR, which we had cloned and were characterizing, as well as for comparison to the GRs from humans, elephant shark and zebrafish. However, although sequencing of the genome of the Australian lungfish (Neoceratodus forsteri), as well as, that of the West African lungfish (Protopterus annectens) were reported in the first three months of 2021, we could not retrieve a GR sequence with a BLAST search of GenBank, when we submitted our research for publication in July 2021. Moreover, we were unsuccessful in cloning the GR from slender African lungfish using a cDNA from the ovary of P. dolloi and PCR primers that had successfully cloned a GR from elephant shark, Xenopus and gar GRs. On October 21, 2021 the nucleotide sequence of West African lungfish (P. annectens) GR was deposited in GenBank. We used this GR sequence to construct PCR primers that successfully cloned the GR from the slender spotted lungfish. Here, we report the sequences of nine P. dolloi GR isoforms and explain the basis for the previous failure to clone a GR from slender African lungfish using PCR primers that cloned the GR from elephant shark, Xenopus and gar. Studies are underway to determine corticosteroid activation of these slender African lungfish GRs.