Prediction Of Half-life Research Articles

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610–0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520–0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119–0.148, MAE-LOOTRAIN = 0.132–0.177, predictive R2 or Q2F1 = 0.538–0.567, Q2F2 = 0.500–0.565, MAETEST = 0.122–0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (−ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.

Study α decay and proton emission based on data-driven symbolic regression

α decay and proton emission were combined using symbolic regression to improve the accuracy of predicting their respective half-lives using two theoretical formulations: (1) adjustable parameters for the universal decay law and universal decay law for proton emission are obtained by regressions with fully-constrained symbolic regressions. (2) New theoretical formulas for calculating the half-life of proton emission and α decay are obtained using unconstrained symbolic regressions combined with nuclear data. Our computational analysis indicates that fully-constrained symbolic regressions and unconstrained symbolic regressions are reliable for specific and general nuclei, respectively, in terms of replicating experimental results, and are sufficiently robust to produce accurate half-life predictions. Unlike other machine learning methods that generate complex and opaque results, our approach integrates physics and machine learning to create interpretable formulas that provide intuitive parametric outcomes and transparent and dependable inferences of half-lives, even in areas with limited experimental data. The test results show that the equation yields accurate results, and can be easily applied to future α decay and proton emission studies.

Investigation of two-proton decay using modified formation probability

In this study, we investigated two-proton radioactivity using the two-potential approach with a cosh-type potential to calculate the half-lives. The depth parameter V0 = 58.405 MeV and diffuseness a = 0.537 fm in the cosh-type nuclear potential show the lowest standard deviation between the calculated and experimental half-lives. We proposed a linear formula for the formation probability using the linear relationship between log10S2p and A1/3d for the angular momentum state l = 0, 2 and 4. The model achieved the lowest standard deviation (σ = 1.09) using this linear formula compared to previous models and empirical formulas. The proposed formula significantly improved the accuracy of half-life predictions by reducing the standard deviation from 1.73 to 1.09. The predicted half-lives exhibit a hindrance factor in the range of -1.62 to 2.42, which is the lowest compared to earlier theoretical predictions. These results indicate that the proposed linear formation probability formula is suitable for reproducing experimental half-lives. The linear formula for formation probability was generalized for different angular momentum states by conducting least squares fit. We extended the half-life and formation probability predictions to 48 nuclei, and the predicted half-lives are in good agreement with the previous five theoretical models and two empirical formula predictions.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

Systematic study of two proton radioactivity within the effective liquid drop model

In this study, we investigated two-proton radioactivity using the effective liquid drop model with varying mass asymmetry shapes and effective inertia coefficients (VMAS-EFF). Gaudin’s expression term for Coulomb potential energy is replaced in the current study’s calculation of potential energy. The VMAS-EFF combination predicts the half-life with the lowest standard deviation ( σ =1.03) compared to other models and previous constant mass asymmetry with Werner-inertial Wheeler’s coefficient combinations. The lowest standard deviation value indicates that the VMAS-EFF combination is suitable for reproducing the experimental data. We extended the half-life predictions to another 54 nuclei and compared them with six theoretical model predictions and two empirical formula predictions. The present work also investigated the decay possibilities using quantum tunnelling by computing the driving potential (V-Q) for various states of angular momentum l = 0, 2, 3, and 4. Found that 6Be, 6B, 8C, 12O,15,16Ne, 19Mg, 22Si, 26S, 30Ar 38,39Ti, 40V, 42Cr, 45Fe, 48,49Ni, 54Zn, 58,59,60Ge and 64Se nuclei have a high chance of emission for two protons, while 7B, 10N, 14F, 17Na, 24P, 28Cl,32K, 34Ca, 36,37Sc, 44Mn, 47Co, 52Cu, 56,57,58Ga, 61,62As nuclei have a lower chance for two proton than one proton decay. The computation of separation energy findings agree well with the driving potential findings.

An empirical formula for the half-lives of one- and two-proton radioactivity

We have proposed an empirical formula for one- and two-proton decays, including the angular momentum term. While investigating the first term in the Viola–Seaborg formula, we found a linear relationship between the logarithmic half-life and the expression [Formula: see text] for each angular momentum state. Furthermore, we obtained a linear relationship between the slope and intercept as functions of the angular momentum (l). Using these relations, we proposed a generalized empirical formula for the half-lives of one- and two-proton radioactivity for all angular momentum states. We analyzed the accuracy of the formula predictions by computing the standard deviation between the predicted and experimental decay half-lives. For the decay of one proton, the standard deviation of the proposed formula is 0.389, while that of the generalized formula is 0.400. These values represent the lowest standard deviations compared to previous models and the empirical formula. In the case of two-proton decay, the standard deviation for the current formula is 1.31, and the generalized formula also exhibits a lower standard deviation of 1.32. This lowest standard deviation value indicates that the proposed formula is suitable for reproducing the experimental half-life. We extended the half-life predictions to another 207 nuclei for one-proton decay and 42 nuclei for two-proton decay cases, which are energetically possible. The estimated half-lives for both one- and two-proton decays agree well with other theoretical predictions.

Insights into the Mechanism, Regio-/Diastereoselectivities and Ligand Role of Nickel-Initiated [3+2] Cycloadditions between Vinylcyclopropane and N-Tosylbenzaldimine

Density functional theory (DFT) was employed to explore the reaction mechanism, regio- and diastereoselectivities of nickel-initiated [3+2] cycloaddition between vinylcyclopropane (VCP) and N-tosylbenzaldimine assisted by phosphine ligands. Four different binding modes of the nickel center to VCP substrate were explored during the ring-opening of VCP, among which the C,C_anti and C,C_syn modes were verified to be the most accessible ones. Further explorations about four different phosphine ligand-assisted reactions based on the two most probable binding modes show that the difference in binding mode of bi- and monodentate phosphine ligands can vary the optimal reaction pathway, especially in the [3+2] cycloaddition process between the ring-opened intermediate and N-tosylbenzaldimine. The formation of C–C and C–N bonds between N-tosylbenzaldimine and the ring-opened intermediate through [3+2] cycloaddition is found to be stepwise, with the former acting as the rate-determining step (RDS) in most cases. Computed free energy barriers of RDS transition states on the optimal path I or II not only give out good predictions for reaction rates and half-lives, but also provide reasonable explanations for the major generation of cis-pyrrolidine. Noncovalent interaction analyses of key stationary points suggest the rate is influenced by both electronic effects and steric hindrance, while the diastereoselectivity is mainly controlled by electronic effects.

Comparative study of neural network and model averaging methods in nuclear β-decay half-life predictions

Nuclear β-decay half-lives are investigated using the two-hidden-layer neural network and compared with the model averaging method. By carefully designing the input and hidden layers of the neural network, the neural network achieves better accuracy of nuclear β-decay half-life predictions and well eliminates the too strong odd–even staggering predicted by the previous neural networks. For nuclei with half-lives less than 1 s, the neural network can describe experimental half-lives within 1.6 times. The half-life predictions of the neural network are further tested with the newly measured half-lives, demonstrating its reliable extrapolation ability not far from the training region. Compared to the model averaging method, the neural network has higher accuracy and smaller uncertainties of half-life predictions in the known region. When extrapolated to the unknown region, the half-life uncertainties of the neural network are still smaller than those of the model averaging method within about 5–10 steps for nuclei with 35 ≲ Z ≲ 90, while the model averaging method has smaller half-life uncertainties for nuclei near the drip line.

Random forest-based prediction of decay modes and half-lives of superheavy nuclei

Random forest-based prediction of decay modes and half-lives of superheavy nuclei

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Quest for a universal cluster preformation formula: A new paradigm for estimating the cluster formation energy

This study presents a holistic picture of the preformation of nuclear clusters with credence to the kinematics of their emissions. Besides the fitting of the preformation formula to reproduce the experimental half-lives, we have investigated the interrelationship between the parameters involved in the cluster decay process for medium, heavy and superheavy nuclei. Based on the established conceptual findings, we propose a new cluster preformation probability (P 0) formula that incorporates all influential parameters of the cluster radioactivity and thus has an edge over the existing formulae in the literature. Further, we hypothesize that a fraction of the decay energy is needed for cluster formation within the parent nucleus. The proposed formula opens a new paradigm to separately estimate the energy contributed during the cluster formation from its emission and thus shows that the contribution of the Q-value splits into three major parts accounting for the energy contributed during the cluster preformation, its emission and recoil of the daughter nucleus. Moreover, the expression P 0 is adept at accommodating the theorized concept of heavy particle radioactivity (HPR). The result reveals that, like α-decay, a proper estimation of the P 0- and Q-value in the cluster studies is enriched with qualitative information about the nuclear structure. However, from the analysis, the Geiger-Nuttall law is not the best compromise in the clustering due to the non-linearity between log10 T 1/2 and , unlike in α-decay. We have demonstrated that with the inclusion of the proposed formula, the half-life predictions from both microscopic R3Y and phenomenological M3Y NN potentials closely agree with the available experimental data and that the slight variation can be traced to their peculiar barrier characteristics.

Endogenous TOM20 Proximity Labeling: A Swiss-Knife for the Study of Mitochondrial Proteins in Human Cells.

Biotin-based proximity labeling approaches, such as BioID, have demonstrated their use for the study of mitochondria proteomes in living cells. The use of genetically engineered BioID cell lines enables the detailed characterization of poorly characterized processes such as mitochondrial co-translational import. In this process, translation is coupled to the translocation of the mitochondrial proteins, alleviating the energy cost typically associated with the post-translational import relying on chaperone systems. However, the mechanisms are still unclear with only few actors identified but none that have been described in mammals yet. We thus profiled the TOM20 proxisome using BioID, assuming that some of the identified proteins could be molecular actors of the co-translational import in human cells. The obtained results showed a high enrichment of RNA binding proteins close to the TOM complex. However, for the few selected candidates, we could not demonstrate a role in the mitochondrial co-translational import process. Nonetheless, we were able to demonstrate additional uses of our BioID cell line. Indeed, the experimental approach used in this study is thus proposed for the identification of mitochondrial co-translational import effectors and for the monitoring of protein entry inside mitochondria with a potential application in the prediction of mitochondrial protein half-life.

A systematic analysis for one proton radioactivity of ground state nuclei

We systematically studied proton radioactivity half-lives in the atomic number range 53≤Z≤83 using macroscopic models such as Coulomb and Proximity potential model, effective liquid drop model (ELDM), Generalized liquid drop model (GLDM), Universal decay law for proton emission (UDLP), Gammow-like model (GLM) and Unified fission model (UFM). The proton decay half-lives produced by the macroscopic models are compared with those of semi-microscopic approach such as DDM3Y and using effective interaction potentials such as JLM, M3Y+EX, R3Y+EX, SLy4, SRG, SKC and SKD. After detail analysis, it is found that among macroscopic models ELDM and semi-empirical formula UDLP produce proton decay half-lives close to experiments. Furthermore, among semi-microscopic approach, DDM3Y produces proton decay half-lives close to the experiments. Thus, Both ELDM and DDM3Y model can be effectively used in the prediction of half-lives of unexplored proton emitters.

Impact of Nuclear β-decay Half-life Uncertainties on the r-process Simulations

The impact of nuclear β-decay half-life uncertainties on the r-process simulations are investigated in the hot wind r-process scenarios. It is found that the theoretical deviations among various half-life predictions are generally smaller and larger than 1 order of magnitude for the unknown nuclei with N ≲ 126 and N ≳ 126, respectively. This will have a significant impact on the r-process freeze-out time and the neutron-to-seed ratio after the freeze-out time. A universal staggering pattern is observed for the r-process abundances with the use of different half-life models before freeze-out. After the freeze-out, nuclear β-decay half-lives remarkably affect the detailed r-process abundance distributions. It is also found that nuclear β-decay half-lives have a remarkable impact on the abundances of transuranium elements, which play an important role in reproducing the second and the rare-earth peaks of solar r-process abundances, and filling the troughs before them by nuclear fissions.

Cluster radioactivity in trans-lead region: A systematic study with modified empirical formulas

The possibility of cluster emission from trans-lead (86≤Z≤96) region of periodic chart has been explored comprehensively by employing few empirical formulas which are modified by adding angular momentum (l) or isospin-dependent (I=(N−Z)/A) or both terms for the calculation of cluster decay half-lives. These modified versions of the formulas are found with lesser χ2 per degree of freedom and root mean-square error, in addition to the smaller values of some other statistical parameters, while compared to their corresponding old versions on available 61 experimental data of cluster radioactivity. By applying the modified version of the formula given by Balasubramaniam et al. (2004) [12], the most accurate formula among these, half-lives of several clusters i.e. isotopes of Be, B, C, N, O, F, Ne, Na, Mg, and Si are predicted systematically for the several isotopes in the trans-lead region. The contest of cluster emission with α-decay has been investigated in form of branching ratio which brings several potential cluster emissions into the probable decay modes of these nuclei. The accurate prediction of half-lives of such clusters is expected to be crucial for the future experimental observations where α-decay is observed dominantly.

Half-life prediction of some neutron-rich exotic nuclei prior to peak A = 130

β-decay is amongst the key properties of nuclei required for the modeling of r-process nucleosynthesis. It also governs the flow of abundances among neighboring isotopic chains of high-mass elements. In the present work, a simple proton-neutron quasi particle random phase approximation (p–n-QRPA) model has been used for the calculation of β-decay half-lives of Rb, Sr, Y and Zr neutron-rich isotopes. For 97−103Rb, 98−107Sr, 101−109Y and 104−112Zr, where the experimental data were available, the half-life values are reproduced with reasonable accuracy. The same set of model parameters are later used to predict half-lives for few neutron-rich nuclei (104−112Rb, 108−113Sr, 110−114Y and 113−115Zr) where measured data is not available. The p–n-QRPA results (including only allowed transitions) are compared with previous calculations (allowed plus forbidden) and exhibit agreement within a factor of 2.0 when compared with the recent available experimental data.

Measuring the β-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region

The β-delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing β-decay half-life (T 1/2) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and β-delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A ≈ 160 in the r-process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of 168Sm and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167–172.

Nonlocality effect in α decay of heavy and superheavy nuclei

The $\ensuremath{\alpha}$-decay half-life of heavy and superheavy nuclei has been investigated using a potential barrier penetration model that adapts semiclassical WKB calculations to incorporate a coordinate-dependent effective mass for the $\ensuremath{\alpha}$ particle. This effect is a consequence of a dynamic property of nonlocality in the particle-nucleus interaction, as implemented in the barrier tunneling calculations of [N. Teruya, S. B. Duarte, and M. M. N. Rodrigues, Phys. Rev. C 93, 024606 (2016)]. Calculations have been performed for a recent set of experimental data of 255 $\ensuremath{\alpha}$-emitting nuclides, all selected with angular momentum $\ensuremath{\ell}=0$ experimentally assigned. Results show a good agreement when compared to experimental half-life data, obtaining a standard deviation of $\ensuremath{\sigma}=0.306$ and fully satisfying the universal systematics, namely, new universal plot and universal decay law systematics, of $\ensuremath{\alpha}$ decay. Additionally, the present model has been applied to make half-life predictions for 34 possible new $\ensuremath{\alpha}$-decay cases.

Predictive power of theoretical models in cluster radioactivity

Many microscopic and macroscopic models are available in the literature to study the cluster radioactivity. The investigation of appropriate theoretical model to study the cluster decay process is an important aspect. We studied cluster-decay in the atomic and mass number range [Formula: see text] and [Formula: see text] using modified generalized liquid drop model (MGLDM), Coulomb and proximity potential model (CPPM) and generalized liquid drop model (GLDM). The results obtained using macroscopic models are compared with that of microscopic models. There are various mass excess tables available in the literature. But indentification of suitable mass excess table for cluster radioactivity is an also important part of this study. The macroscopic models, such as MGLDM, CPPM and GLDM, were used to analyze cluster-decay half-lives. Along with this, microscopic and semi-empirical relations were also investigated. The standard deviation is evaluated in macroscopic, microscopic and semi-empirical formulae. A detailed investigation shows that among macroscopic model-MGLDM, macroscopic-RMFM and in case of semi-empirical formulae, AZF produces less deviation. Hence, these results give an insight into prediction of cluster decay half-lives in heavy and superheavy region for unknown nuclei.

Beta-decay half-lives of the isotopes close to the neutron drip line and astrophysical implications

In this paper, we examined the β −-decay half-lives of 94 extremely neutron-rich isotopes with Z = 26 − 57 close to the neutron drip line, which are important for the r-process calculations. The half-lives were calculated using four semi-empirical models and compared to those based on the FRDM+QRPA approach and available measured data. The impact of the difference in the models on the half-life predictions was investigated. We found that theoretical calculations for the β-decay half-life have a large deviation, up to 60%, which is mostly similar to that in measurements. The half-lives of the investigated nuclei are ranging from a few to hundreds of milliseconds. The r-process abundances in various astrophysical scenarios were calculated by using the predicted half-lives. The half-life uncertainty due to different models results in a large deviation in the isotopic abundance, specially for the isotopes in the mass range of A > 210. The shell closures in 76Fe is still a doubt due to the discrepancy in the trends of the half-life and paring gap while a closed-shell at N = 82 in 127Rh is possible. The results of this study also notice that it is a challenge for measuring precisely the masses of 106Rb, 116,117Nb, 122Tc, and 128Rh because of their short half-lives.