Nuclear shell evolution and in-medium NN interaction

We report on a quantitative study of the evolution of the nuclear shell structure, in particular, effective single-particle energies (ESPEs), based on the spin-tensor decomposition of an effective two-body shell-model interaction. While the global trend of the ESPEs is mainly due to the central term of the effective interaction, variations of shell gaps invoke various components of the in-medium NN force. From a detailed analysis of a well-fitted realistic interaction in the sdpf shell-model space, two most important contributions for the evolution of the N = 20 and N = 28 shell gaps are confirmed to be the central term and the tensor term. The role of the latter is dominant to explain the energy shift of spin-orbit partners. Spin-tensor analysis of microscopic effective interactions in sd, pf, and gds shell-model spaces, contrasted with that of the phenomenologically adjusted ones, shows no evidence of amplification of the tensor component contribution; however, it points toward the neglect of three-body forces in the present microscopic interactions

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Cobalt-Porphyrin Catalyzed Electrochemical Reduction of Carbon Dioxide in Water II: Mechanism from First Principles

We apply first principles computational techniques to analyze the two-electron, multi-step, electrochemical reduction of CO2 to CO in water using cobalt porphyrin as a catalyst. Density Functional Theory calculations with hybrid functionals and dielectric continuum solvation are used to determine the steps at which electrons are added. This information is corroborated with ab initio molecular dynamics simulations in an explicit aqueous environment which reveal the critical role of water in stabilizing a key intermediate formed by CO2 bound to cobalt. Using potential of mean force calculations, the intermediate is found to spontaneously accept a proton to form a carboxylate acid group at pH<9.0, and the subsequent cleavage of a C-OH bond to form CO is exothermic and associated with a small free energy barrier. These predictions suggest that the proposed reaction mechanism is viable if electron transfer to the catalyst is sufficiently fast. The variation in cobalt ion charge and spin states during bond breaking, DFT+U treatment of cobalt 3d orbitals, and the need for computing electrochemical potentials are emphasized.Comment: 33 pages, 7 figure

The Healing of Nerves:

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Digestion Activity in Mental Diseases:

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The Treatment of Neuralgias With Chlormethyl:

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Digestion Activity in Mental Diseases:

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Keyed Non-Parametric Hypothesis Tests

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $\kappa$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $\kappa$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.Comment: Paper published in NSS 201

Performance Analysis of Adaptive Notch Filter Active Damping Methods for Grid-Connected Converters under a Varying Grid Impedance

Grid connected converters commonly use LCL filters for harmonic content suppression. However, associated with such filters is a resonant frequency at which the gain value increases significantly. To mitigate this problem, a notch filter is introduced into the current control loop of the converter. When tuned to the LCL resonant frequency, it introduces an opposing notch, thereby neutralizing the resonance effect. To ensure robustness of the control system, the notch filter must be made adaptive. This will ensure any variation in the resonant frequency, either due to a change in grid impedance or aging of components, can be tracked accurately. This paper provides two novel methods of online tuning for the adaptive notch filter using grid impedance estimation and discrete Fourier transform (DFT) techniques. Simulation results show that both methods are capable of fast and accurate detection of the resonant frequency, for varying strengths of the grid