Mimicking the surface and prebiotic chemistry of early Earth using flow chemistry.

When considering life's aetiology, the first questions that must be addressed are "how?" and "where?" were ostensibly complex molecules, considered necessary for life's beginning, constructed from simpler, more abundant feedstock molecules on primitive Earth. Previously, we have used multiple clues from the prebiotic synthetic requirements of (proto)biomolecules to pinpoint a set of closely related geochemical scenarios that are suggestive of flow and semi-batch chemistries. We now wish to report a multistep, uninterrupted synthesis of a key heterocycle (2-aminooxazole) en route to activated nucleotides starting from highly plausible, prebiotic feedstock molecules under conditions which mimic this scenario. Further consideration of the scenario has uncovered additional pertinent and novel aspects of prebiotic chemistry, which greatly enhance the efficiency and plausibility of the synthesis

Thiophosphate photochemistry enables prebiotic access to sugars and terpenoid precursors.

Acknowledgements: We would like to thank S. J. Mojzsis for helpful discussions and T. Rutherford for invaluable assistance with NMR spectroscopy. This work was supported by the Medical Research Council, as part of United Kingdom Research and Innovation (also known as UK Research and Innovation; grant no. MC_UP_A024_1009 to J.D.S.), and a grant from the Simons Foundation (grant no. 290362 to J.D.S.). For the purpose of open access, the MRC Laboratory of Molecular Biology has applied a CC BY public copyright licence to any author accepted manuscript version arising.Over the past few years, evidence has accrued that demonstrates that terrestrial photochemical reactions could have provided numerous (proto)biomolecules with implications for the origin of life. This chemistry simply relies on UV light, inorganic sulfur species and hydrogen cyanide. Recently, we reported that, under the same conditions, reduced phosphorus species, such as those delivered by meteorites, can be oxidized to orthophosphate, generating thiophosphate in the process. Here we describe an investigation of the properties of thiophosphate as well as additional possible means for its formation on primitive Earth. We show that several reported prebiotic reactions, including the photoreduction of thioamides, carbonyl groups and cyanohydrins, can be markedly improved, and that tetroses and pentoses can be accessed from hydrogen cyanide through a Kiliani-Fischer-type process without progressing to higher sugars. We also demonstrate that thiophosphate allows photochemical reductive aminations, and that thiophosphate chemistry allows a plausible prebiotic synthesis of the C5 moieties used in extant terpene and terpenoid biosynthesis, namely dimethylallyl alcohol and isopentenyl alcohol

Azoles as Auxiliaries and Intermediates in Prebiotic Nucleoside Synthesis.

4,5-Dicyanoimidazole and 2-aminothiazole are azoles that have previously been implicated in prebiotic nucleotide synthesis. The former compound is a byproduct of adenine synthesis, and the latter compound has been shown to be capable of separating C2 and C3 sugars via crystallization as their aminals. We now report that the elusive intermediate cyanoacetylene can be captured by 4,5-dicyanoimidazole and accumulated as the crystalline compound N-cyanovinyl-4,5-dicyanoimidazole, thus providing a solution to the problem of concentration of atmospherically formed cyanoacetylene. Importantly, this intermediate is a competent cyanoacetylene surrogate, reacting with ribo-aminooxazoline in formamide to give ribo-anhydrocytidine ─ an intermediate in the divergent synthesis of purine and pyrimidine nucleotides. We also report a prebiotically plausible synthesis of 2-aminothiazole and examine the mechanism of its formation. The utilization of each of these azoles enhances the prebiotic synthesis of ribonucleotides, while their syntheses comport with the cyanosulfidic scenario we have previously described

Support Distribution Machines

<p>Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algorithms to operate on groups of data points. We suggest treating a group of data points as a set of i.i.d. samples from an underlying feature distribution for the group. Our approach is to generalize kernel machines from vectorial inputs to i.i.d. sample sets of vectors. For this purpose, we use a nonparametric estimator that can consistently estimate the inner product and certain kernel functions of two distributions. The projection of the estimated Gram matrix to the cone of semi-definite matrices enables us to employ the kernel trick, and hence use kernel machines for classification, regression, anomaly detection, and low-dimensional embedding in the space of distributions. We present several numerical experiments both on real and simulated datasets to demonstrate the advantages of our new approach.</p