Publisher Correction: Universal probabilistic programming offers a powerful approach to statistical phylogenetics

AbstractStatistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here, we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

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Universal probabilistic programming offers a powerful approach to statistical phylogenetics

10.1101/2020.06.16.154443 ◽

2020 ◽

Cited By ~ 1

Author(s):

Fredrik Ronquist ◽

Jan Kudlicka ◽

Viktor Senderov ◽

Johannes Borgström ◽

Nicolas Lartillot ◽

...

Keyword(s):

Programming Languages ◽

Graphical Models ◽

Sequential Monte Carlo ◽

Full Range ◽

Efficient Estimation ◽

Probabilistic Programming ◽

Automated Generation ◽

Inference Algorithms ◽

Powerful Approach ◽

Inference Strategy

Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

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Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles

Nanoscale ◽

10.1039/d1nr03232a ◽

2021 ◽

Author(s):

Alexander Skorikov ◽

Wouter Heyvaert ◽

Wiebke Albrecht ◽

Daan Pelt ◽

Sara Bals

Keyword(s):

Deep Learning ◽

Electron Tomography ◽

Energy Dispersive ◽

Elemental Distribution ◽

X Ray ◽

Powerful Approach ◽

Dose Efficiency

The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex,...

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Paradoxes of probabilistic programming: and how to condition on events of measure zero with infinitesimal probabilities

Proceedings of the ACM on Programming Languages ◽

10.1145/3434339 ◽

2021 ◽

Vol 5 (POPL) ◽

pp. 1-26

Author(s):

Jules Jacobs

Keyword(s):

Measure Zero ◽

Probabilistic Programming

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HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of “olive quick decline syndrome”

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03279-7 ◽

2021 ◽

Author(s):

Sabrina Di Masi ◽

Giuseppe E. De Benedetto ◽

Cosimino Malitesta ◽

Maria Saponari ◽

Cinzia Citti ◽

...

Keyword(s):

Bacterial Infections ◽

High Performance ◽

High Resolution Mass Spectrometry ◽

Xylella Fastidiosa ◽

Long Chain Fatty Acids ◽

Powerful Approach ◽

Diffusible Signal Factor ◽

Profile Changes ◽

Olive Quick Decline Syndrome ◽

Olive Trees

AbstractOlive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants. Graphical abstract

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Improved in vivo imaging method for individual islets across the mouse pancreas reveals a heterogeneous insulin secretion response to glucose

Scientific Reports ◽

10.1038/s41598-020-79727-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Henriette Frikke-Schmidt ◽

Peter Arvan ◽

Randy J. Seeley ◽

Corentin Cras-Méneur

Keyword(s):

Insulin Secretion ◽

In Vivo Imaging ◽

Motion Blur ◽

Isolated Islets ◽

Imaging Method ◽

Glucose Stimulation ◽

Powerful Approach ◽

Glucose Challenge ◽

The Impact

AbstractWhile numerous techniques can be used to measure and analyze insulin secretion in isolated islets in culture, assessments of insulin secretion in vivo are typically indirect and only semiquantitative. The CpepSfGFP reporter mouse line allows the in vivo imaging of insulin secretion from individual islets after a glucose stimulation, in live, anesthetized mice. Imaging the whole pancreas at high resolution in live mice to track the response of each individual islet over time includes numerous technical challenges and previous reports were only limited in scope and non-quantitative. Elaborating on this previous model—through the development of an improved methodology addressing anesthesia, temperature control and motion blur—we were able to track and quantify longitudinally insulin content throughout a glucose challenge in up to two hundred individual islets simultaneously. Through this approach we demonstrate quantitatively for the first time that while isolated islets respond homogeneously to glucose in culture, their profiles differ significantly in vivo. Independent of size or location, some islets respond sharply to a glucose stimulation while others barely secrete at all. This platform therefore provides a powerful approach to study the impact of disease, diet, surgery or pharmacological treatments on insulin secretion in the intact pancreas in vivo.

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Gran1: A Granulysin-Derived Peptide with Potent Activity against Intracellular Mycobacterium tuberculosis

International Journal of Molecular Sciences ◽

10.3390/ijms22168392 ◽

2021 ◽

Vol 22 (16) ◽

pp. 8392

Author(s):

Reiner Noschka ◽

Fanny Wondany ◽

Gönül Kizilsavas ◽

Tanja Weil ◽

Gilbert Weidinger ◽

...

Keyword(s):

Antimicrobial Activity ◽

Mycobacterium Tuberculosis ◽

Developmental Toxicity ◽

Super Resolution ◽

In Vivo Models ◽

Large Size ◽

Acid Fragment ◽

Powerful Approach ◽

Target Effects

Granulysin is an antimicrobial peptide (AMP) expressed by human T-lymphocytes and natural killer cells. Despite a remarkably broad antimicrobial spectrum, its implementation into clinical practice has been hampered by its large size and off-target effects. To circumvent these limitations, we synthesized a 29 amino acid fragment within the putative cytolytic site of Granulysin (termed “Gran1”). We evaluated the antimicrobial activity of Gran1 against the major human pathogen Mycobacterium tuberculosis (Mtb) and a panel of clinically relevant non-tuberculous mycobacteria which are notoriously difficult to treat. Gran1 efficiently inhibited the mycobacterial proliferation in the low micro molar range. Super-resolution fluorescence microscopy and scanning electron microscopy indicated that Gran1 interacts with the surface of Mtb, causing lethal distortions of the cell wall. Importantly, Gran1 showed no off-target effects (cytokine release, chemotaxis, cell death) in primary human cells or zebrafish embryos (cytotoxicity, developmental toxicity, neurotoxicity, cardiotoxicity). Gran1 was selectively internalized by macrophages, the major host cell of Mtb, and restricted the proliferation of the pathogen. Our results demonstrate that the hypothesis-driven design of AMPs is a powerful approach for the identification of small bioactive compounds with specific antimicrobial activity. Gran1 is a promising component for the design of AMP-containing nanoparticles with selective activity and favorable pharmacokinetics to be pushed forward into experimental in vivo models of infectious diseases, most notably tuberculosis.

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Anisotropic nanocrystal shape and ligand design for co-assembly

Science Advances ◽

10.1126/sciadv.abf9402 ◽

2021 ◽

Vol 7 (23) ◽

pp. eabf9402

Author(s):

Katherine C. Elbert ◽

William Zygmunt ◽

Thi Vo ◽

Corbin M. Vara ◽

Daniel J. Rosen ◽

...

Keyword(s):

Rational Design ◽

Ligand Design ◽

Building Blocks ◽

Two Dimensional ◽

Secondary System ◽

Assembly Design ◽

Ligand Shell ◽

Powerful Approach ◽

Anisotropic Systems ◽

Direct Materials

The use of nanocrystal (NC) building blocks to create metamaterials is a powerful approach to access emergent materials. Given the immense library of materials choices, progress in this area for anisotropic NCs is limited by the lack of co-assembly design principles. Here, we use a rational design approach to guide the co-assembly of two such anisotropic systems. We modulate the removal of geometrical incompatibilities between NCs by tuning the ligand shell, taking advantage of the lock-and-key motifs between emergent shapes of the ligand coating to subvert phase separation. Using a combination of theory, simulation, and experiments, we use our strategy to achieve co-assembly of a binary system of cubes and triangular plates and a secondary system involving two two-dimensional (2D) nanoplates. This theory-guided approach to NC assembly has the potential to direct materials choices for targeted binary co-assembly.

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