Properties of Reactive Oxygen Species by Quantum Monte Carlo

The electronic properties of the oxygen molecule, in its singlet and triplet states, and of many small oxygen-containing radicals and anions have important roles in different fields of Chemistry, Biology and Atmospheric Science. Nevertheless, the electronic structure of such species is a challenge for ab-initio computational approaches because of the difficulties to correctly describe the statical and dynamical correlation effects in presence of one or more unpaired electrons. Only the highest-level quantum chemical approaches can yield reliable characterizations of their molecular properties, such as binding energies, equilibrium structures, molecular vibrations, charge distribution and polarizabilities. In this work we use the variational Monte Carlo (VMC) and the lattice regularized Monte Carlo (LRDMC) methods to investigate the equilibrium geometries and molecular properties of oxygen and oxygen reactive species. Quantum Monte Carlo methods are used in combination with the Jastrow Antisymmetrized Geminal Power (JAGP) wave function ansatz, which has been recently shown to effectively describe the statical and dynamical correlation of different molecular systems. In particular we have studied the oxygen molecule, the superoxide anion, the nitric oxide radical and anion, the hydroxyl and hydroperoxyl radicals and their corresponding anions, and the hydrotrioxyl radical. Overall, the methodology was able to correctly describe the geometrical and electronic properties of these systems, through compact but fully-optimised basis sets and with a computational cost which scales as $N^3-N^4$, where $N$ is the number of electrons. This work is therefore opening the way to the accurate study of the energetics and of the reactivity of large and complex oxygen species by first principles

Perilous Predicting

This article considers the difficulty of drafting future rental provisions as illustrated in California National Bank v. Woodbridge Plaza, LLC

Perilous Predicting

This article considers the difficulty of drafting future rental provisions as illustrated in California National Bank v. Woodbridge Plaza, LLC

Geometric isomers of chloro(6-methyl-1,4,8,11-tetraazacyclotetradecane-6-amine)cobalt(III) tetrachlorozincate(II)

The crystal structures of a pair of cis and trans isomers of the macrocyclic chloropentaamine title complex, as their tetrachlorozincate(II) salts, [CoCl(C11H27N5)][ZnCl4], are reported. The two distinct isomeric forms lead to significant variations in the Co-N bond lengths and, furthermore, hydrogen bonding between the complex ions is influenced by the folded (cis) or planar (trans) conformations of the coordinated ligand

Sources of variability in essential oil composition of Ocimum americanum and Ocimum tenuiflorum

Basil has traditionally been used for a long time in medicine and gastronomy. Essential oil is the most important active substance of the drug, which influences the aroma and the effect of the plant. Although the compositions of essential oils vary in different basil cultivars, the main components are oxygenated monoterpenes and phenylpropane derivates. The high chemical variation is most likely caused by interspecific hybridization. Various factors, like genetic background, ontogenesis, morphogenesis, abiotic factors, essential oil extraction method, drying, and storage, are responsible for the variant essential oil composition

Raman-induced Kerr-effect dual-comb spectroscopy

We report on the first demonstration of nonlinear dual-frequency-comb spectroscopy. In multi-heterodyne femtosecond Raman-induced Kerr-effect spectroscopy, the Raman gain resulting from the coherent excitation of molecular vibrations by a spectrally-narrow pump is imprinted onto the femtosecond laser frequency comb probe spectrum. The birefringence signal induced by the nonlinear interaction of these beams and the sample is heterodyned against a frequency comb local oscillator with a repetition frequency slightly different from that of the comb probe. Such time-domain interference provides multiplex access to the phase and amplitude Raman spectra over a broad spectral bandwidth within a short measurement time. Experimental demonstration, at a spectral resolution of 200 GHz, a measurement time of 293 {\mu}s and a sensitivity of 10^-6, is given on liquid samples exhibiting a C-H stretch Raman shift.Comment: 7 pages, 4 figure

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders

Algorithmic encoding of protected characteristics in chest X-ray disease detection models

Background It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable correlations in the (historical) training data. It remains unclear how we can establish whether such information is actually used. Besides the scarcity of data from underserved populations, very little is known about how dataset biases manifest in predictive models and how this may result in disparate performance. This article aims to shed some light on these issues by exploring methodology for subgroup analysis in image-based disease detection models. Methods We utilize two publicly available chest X-ray datasets, CheXpert and MIMIC-CXR, to study performance disparities across race and biological sex in deep learning models. We explore test set resampling, transfer learning, multitask learning, and model inspection to assess the relationship between the encoding of protected characteristics and disease detection performance across subgroups. Findings We confirm subgroup disparities in terms of shifted true and false positive rates which are partially removed after correcting for population and prevalence shifts in the test sets. We find that transfer learning alone is insufficient for establishing whether specific patient information is used for making predictions. The proposed combination of test-set resampling, multitask learning, and model inspection reveals valuable insights about the way protected characteristics are encoded in the feature representations of deep neural networks. Interpretation Subgroup analysis is key for identifying performance disparities of AI models, but statistical differences across subgroups need to be taken into account when analyzing potential biases in disease detection. The proposed methodology provides a comprehensive framework for subgroup analysis enabling further research into the underlying causes of disparities. Funding European Research Council Horizon 2020, UK Research and Innovation

Monitoring of chemical parameters of qualitative pasta samples containing millet flours during storage experiments

Millet has attracted a great deal of interest due to its valuable agricultural, nutritional, and functional properties. In this study the aim was the investigation of millet usability in dry pasta products. Chemical, enzymological, and sensory parameters were measured and monitored in Triticum aestivum, Triticum durum, and millet containing pasta products during a 12-month-long storage period. According to our results, during the storage, millet had a strong effect on different parameters: because of increased acid value, the shelf life was reduced, and millet significantly influenced the pH value and the water soluble polyphenol content. The highest scores were measured in T. durum and T. durum-millet pasta samples in the sensory test, while the T. aestivum-millet mixture pasta got the lowest scores. Also in our experiment we tested how the drying temperature modifies polyphenol oxidase enzyme (PPO) activity right after drying and during storage. The samples containing millet flour had higher PPO activity in all cases after drying, while pasta made with T. durum had the lowest PPO activity. Our results showed that drying temperature has a significant impact on PPO activity

Precision spectroscopy of the 3s-3p fine structure doublet in Mg+

We apply a recently demonstrated method for precision spectroscopy on strong transitions in trapped ions to measure both fine structure components of the 3s-3p transition in 24-Mg+ and 26-Mg+. We deduce absolute frequency reference data for transition frequencies, isotope shifts and fine structure splittings that are in particular useful for comparison with quasar absorption spectra, which test possible space-time variations of the fine structure constant. The measurement accuracy improves previous literature values, when existing, by more than two orders of magnitude