Saddle point localization of molecular wavefunctions

The quantum mechanical description of isomerization is based on bound eigenstates of the molecular potential energy surface. For the near-minimum regions there is a textbook-based relationship between the potential and eigenenergies. Here we show how the saddle point region that connects the two minima is encoded in the eigenstates of the model quartic potential and in the energy levels of the [H, C, N] potential energy surface. We model the spacing of the eigenenergies with the energy dependent classical oscillation frequency decreasing to zero at the saddle point. The eigenstates with the smallest spacing are localized at the saddle point. The analysis of the HCN???HNC isomerization states shows that the eigenstates with small energy spacing relative to the effective (v1, v3, l) bending potentials are highly localized in the bending coordinate at the transition state. These spectroscopically detectable states represent a chemical marker of the transition state in the eigenenergy spectrum. The method developed here provides a basis for modeling characteristic patterns in the eigenenergy spectrum of bound states

Direct image to subtype prediction for brain tumors using deep learning

BACKGROUND: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. METHODS: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. RESULTS: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. CONCLUSIONS: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure

Exploring the role of smartphone technology for citizen science in agriculture

Citizen science is the involvement of citizens, such as farmers, in the research process. Citizen science has become increasingly popular recently, supported by the proliferation of mobile communication technologies such as smartphones. However, citizen science methodologies have not yet been widely adopted in agricultural research. Here, we conducted an online survey with 57 British and French farmers in 2014. We investigated (1) farmer ownership and use of smartphone technologies, (2) farmer use of farm-specific management apps, and (3) farmer interest and willingness to participate in agricultural citizen science projects. Our results show that 89 % respondents owned a smartphone, 84 % used it for farm management, and 72 % used it on a daily basis. Fifty-nine percent engaged with farm-specific apps, using on average four apps. Ninety-three percent respondents agreed that citizen science was a useful methodology for data collection, 93 % for real-time monitoring, 83 % for identification of research questions, 72 % for experimental work, and 72 % for wildlife recording. Farmers also showed strong interest to participate in citizen science projects, often willing to commit substantial amounts of time. For example, 54 % of British respondents were willing to participate in farmland wildlife recording once a week or monthly. Although financial support was not always regarded as necessary, experimental work was the most likely activity for which respondents thought financial support would be essential. Overall, this is the first study to quantify and explore farmers' use of smartphones for farm management, and document strong support for farm-based citizen science projects. (Résumé d'auteur

Reduction in camera-specific variability in [123I]FP-CIT SPECT outcome measures by image reconstruction optimized for multisite settings: impact on age-dependence of the specific binding ratio in the ENC-DAT database of healthy controls

Purpose Quantitative estimates of dopamine transporter availability, determined with [123I]FP-CIT SPECT, depend on the SPECT equipment, including both hardware and (reconstruction) software, which limits their use in multicentre research and clinical routine. This study tested a dedicated reconstruction algorithm for its ability to reduce camera-specific intersubject variability in [123I]FP-CIT SPECT. The secondary aim was to evaluate binding in whole brain (excluding striatum) as a reference for quantitative analysis. Methods Of 73 healthy subjects from the European Normal Control Database of [123I]FP-CIT recruited at six centres, 70 aged between 20 and 82 years were included. SPECT images were reconstructed using the QSPECT software package which provides fully automated detection of the outer contour of the head, camera-specific correction for scatter and septal penetration by transmission-dependent convolution subtraction, iterative OSEMreconstruction including attenuation correction, and camera-specific Bto kBq/ml^ calibration. LINK and HERMES reconstruction were used for head-to-head comparison. The specific striatal [123I]FP-CIT binding ratio (SBR) was computed using the Southampton method with binding in the whole brain, occipital cortex or cerebellum as the reference. The correlation between SBR and age was used as the primary quality measure. Results The fraction of SBR variability explained by age was highest (1) with QSPECT, independently of the reference region, and (2) with whole brain as the reference, independently of the reconstruction algorithm. Conclusion QSPECT reconstruction appears to be useful for reduction of camera-specific intersubject variability of [123I]FP-CIT SPECT in multisite and single-site multicamera settings. Whole brain excluding striatal binding as the reference provides more stable quantitative estimates than occipital or cerebellar binding

No difference in striatal dopamine transporter availability between active smokers, ex-smokers and non-smokers using [123I]FP-CIT (DaTSCAN) and SPECT

Background: Mesolimbic and nigrostriatal dopaminergic pathways play important roles in both the rewarding and conditioning effects of drugs. The dopamine transporter (DAT) is of central importance in regulating dopaminergic neurotransmission and in particular in activating the striatal D2-like receptors. Molecular imaging studies of the relationship between DAT availability/dopamine synthesis capacity and active cigarette smoking have shown conflicting results. Through the collaboration between 13 SPECT centres located in 10 different European countries, a database of FP-CIT-binding in healthy controls was established. We used the database to test the hypothesis that striatal DAT availability is changed in active smokers compared to non-smokers and ex-smokers. Methods: A total of 129 healthy volunteers were included. Subjects were divided into three categories according to past and present tobacco smoking: (1) non-smokers (n = 64), (2) ex-smokers (n = 39) and (3) active smokers (n = 26). For imaging of the DAT availability, we used [123I]FP-CIT (DaTSCAN) and single photon emission computed tomography (SPECT). Data were collected in collaboration between 13 SPECT centres located in 10 different European countries. The striatal measure of DAT availability was analyzed in a multiple regression model with age, SPECT centre and smoking as predictor. Results: There was no statistically significant difference in DAT availability between the groups of active smokers, ex-smokers and non-smokers (p = 0.34). Further, we could not demonstrate a significant association between striatal DAT and the number of cigarettes per day or total lifetime cigarette packages in smokers and ex-smokers. Conclusion: Our results do not support the hypothesis that large differences in striatal DAT availability are present in smokers compared to ex-smokers and healthy volunteers with no history of smoking

A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT" Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020). A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network.authorsversionpublishe

Position and momentum mapping of vibrations in graphene nanostructures in the electron microscope

Propagating atomic vibrational waves, phonons, rule important thermal, mechanical, optoelectronic and transport characteristics of materials. Thus the knowledge of phonon dispersion, namely the dependence of vibrational energy on momentum is a key ingredient to understand and optimize the material's behavior. However, despite its scientific importance in the last decade, the phonon dispersion of a freestanding monolayer of two dimensional (2D) materials such as graphene and its local variations has still remained elusive because of experimental limitations of vibrational spectroscopy. Even though electron energy loss spectroscopy (EELS) in transmission has recently been shown to probe the local vibrational charge responses, these studies are yet limited to polar materials like boron nitride or oxides, in which huge signals induced by strong dipole moments are present. On the other hand, measurements on graphene performed by inelastic x-ray (neutron) scattering spectroscopy or EELS in reflection do not have any spatial resolution and require large microcrystals. Here we provide a new pathway to determine the phonon dispersions down to the scale of an individual freestanding graphene monolayer by mapping the distinct vibration modes for a large momentum transfer. The measured scattering intensities are accurately reproduced and interpreted with density functional perturbation theory (DFPT). Additionally, a nanometre-scale mapping of selected momentum (q) resolved vibration modes using graphene nanoribbon structures has enabled us to spatially disentangle bulk, edge and surface vibrations