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1. The BRIKEN Project: Extensive Measurements of $\beta $-delayed Neutron Emitters for the Astrophysical r Process

   https://doi.org/10.5506/APhysPolB.49.417  

   Tain, J.L. ; Agramunt, J. ; Ahn, D.S. ; Algora, A. ; Allmond, J.M. ; Baba, H. ; Bae, S. ; Brewer, N.T. ; Caballero Folch, R. ; Calvino, F. ; et al ( January 2018 , Acta Physica Polonica B)
2. Evidence for Gamow-Teller Decay of Ni78 Core from Beta-Delayed Neutron Emission Studies

   https://doi.org/10.1103/PhysRevLett.117.092502  

   Madurga, M. ; Paulauskas, S. V. ; Grzywacz, R. ; Miller, D. ; Bardayan, D. W. ; Batchelder, J. C. ; Brewer, N. T. ; Cizewski, J. A. ; Fijałkowska, A. ; Gross, C. J. ; et al ( August 2016 , Physical Review Letters)
3. Beta–Beta Bounds: Finite-Blocklength Analog of the Golden Formula

   https://doi.org/10.1109/TIT.2018.2837104  

   Yang, Wei ; Collins, Austin ; Durisi, Giuseppe ; Polyanskiy, Yury ; Poor, H. Vincent ( September 2018 , IEEE Transactions on Information Theory)
4. Charge-carrier collective motion in germanium detectors for $$\beta \beta $$-decay searches

   https://doi.org/10.1140/epjc/s10052-021-08889-0  

   Comellato, Tommaso ; Agostini, Matteo ; Schönert, Stefan ( January 2021 , The European Physical Journal C)

   null (Ed.)

   Abstract The time analysis of the signal induced by the drift of charge carriers in high purity germanium detectors provides information on the event topology. Millions of charge carriers are produced in a typical event. Their initial distribution, stochastic diffusion and Coulomb self-repulsion affect the time structure of the signal. We present a comprehensive study of these effects and evaluate their impact on the event discrimination capabilities for the three geometries which will be used in the Legend  experiment for neutrinoless double-beta decay. 

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5. Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

   https://doi.org/10.1371/journal.pone.0233509  

   Yang, Jordan ; Naik, Nandita ; Patel, Jagdish S. ; Wylie, Christopher S. ; Gu, Wenze ; Huang, Jessie ; Ytreberg, F. Marty ; Naik, Mandar T. ; Weinreich, Daniel M. ; Rubenstein, Brenda M. ( May 2020 , PloS one)

   null (Ed.)

   One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming 

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