McMahon, S., 2019. Earth's earliest and deepest purported fossils may be iron-mineralized chemical gardens. Proceedings of the Royal Society B: Biological Sciences 286, 20192410.

https://doi.org/10.1098/rspb.2019.2410 Recognizing fossil microorganisms is essential to the study of life's origin and evolution and to the ongoing search for life on Mars. Purported fossil microbes in ancient rocks include common assemblages of iron-mineral filaments and tubes. Recently, such assemblages have been interpreted to represent Earth's oldest body fossils, Earth's oldest fossil fungi, and Earth's best analogues for fossils that might form in the basaltic Martian subsurface. Many of these putative fossils exhibit hollow circular cross-sections, lifelike (non-crystallographic, constant- thickness, and bifurcate) branching, anastomosis, nestedness within 'sheaths', and other features interpreted as strong evidence for a biological origin, since no abiotic process consistent with the composition of the filaments has been shown to produce these specific lifelike features either in nature or in the laboratory. Here, I show experimentally that abiotic chemical gardening can mimic such purported fossils in both morphology and composition. In particular, chemical gardens meet morphological criteria previously proposed to establish biogenicity, while also producing the precursors to the iron minerals most commonly constitutive of filaments in the rock record. Chemical gardening is likely to occur in nature. Such microstructures should therefore not be assumed to represent fossil microbes without independent corroborating evidence.

Mello, F.d.S., Friaça, A.C.S., 2020. The end of life on Earth is not the end of the world: converging to an estimate of life span of the biosphere? International Journal of Astrobiology 19, 25-42.

https://doi.org/10.1017/S1473550419000120 Environmental conditions have changed in the past of our planet but were not hostile enough to extinguish life. In the future, an aged Earth and a more luminous Sun may lead to harsh or even uninhabitable conditions for life. In order to estimate the life span of the biosphere we built a minimal model of the co-evolution of the geosphere, atmosphere and biosphere of our planet, taking into account temperature boundaries, CO2 partial pressure lower limits for C3 and C4 plants, and the presence of enough surface water. Our results indicate that the end of the biosphere will happen long before the Sun becomes a red giant, as the biosphere faces increasingly more difficult conditions in the future until its collapse due to high temperatures. The lower limit for CO2 partial pressure for C3 plants will be reached in 170(+ 320, - 110) Myr, followed by the C4 plants limit in 840(+ 270, - 100) Myr. The mean surface temperature will reach 373 K in 1.63(+ 0.14, - 0.05) Gyr, a point that would mark the extinction of the biosphere. Water loss due to internal geophysical processes will not be dramatic, implying almost no variation in the surface ocean mass and ocean depth for the next 1.5 billion years. Our predictions show qualitative convergence and some quantitative agreement with results found in the literature, but there is considerable scattering in the scale of hundreds of millions of years for all the criteria devised. Even considering these uncertainties, the end of the biosphere will hardly happen sooner than 1.5 Gyr.

Melnikov, A.D., Tsentalovich, Y.P., Yanshole, V.V., 2020. Deep learning for the precise peak detection in high- resolution LC - MS data. Analytical Chemistry 92, 588-592.

https://doi.org/10.1021/acs.analchem.9b04811 This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography - mass spectrometry (LC - MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the peakonly present work, we developed an algorithm named , which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high- resolution LC - MS data for the purposes of metabolomics, but potentially it can be applied with several Peakonly adaptations in other fields, which utilize high- resolution GC- or LC- MS techniques. is freely available on GitHub (https://github.com/arseha/peakonly) under an MIT license.