Post-Newtonian, Quasi-Circular Binary Inspirals in Quadratic Modified Gravity

We consider a general class of quantum gravity-inspired, modified gravity theories, where the Einstein-Hilbert action is extended through the addition of all terms quadratic in the curvature tensor coupled to scalar fields with standard kinetic energy. This class of theories includes Einstein-Dilaton-Gauss-Bonnet and Chern-Simons modified gravity as special cases. We analytically derive and solve the coupled field equations in the post-Newtonian approximation, assuming a comparable-mass, spinning black hole binary source in a quasi-circular, weak-field/slow-motion orbit. We find that a naive subtraction of divergent piece associated with the point-particle approximation is ill-suited to represent compact objects in these theories. Instead, we model them by appropriate effective sources built so that known strong-field solutions are reproduced in the far-field limit. In doing so, we prove that black holes in Einstein-Dilaton-Gauss-Bonnet and Chern-Simons theory can have hair, while neutron stars have no scalar monopole charge, in diametrical opposition to results in scalar-tensor theories. We then employ techniques similar to the direct integration of the relaxed Einstein equations to obtain analytic expressions for the scalar field, metric perturbation, and the associated gravitational wave luminosity measured at infinity. We find that scalar field emission mainly dominates the energy flux budget, sourcing electric-type (even-parity) dipole scalar radiation and magnetic-type (odd-parity) quadrupole scalar radiation, correcting the General Relativistic prediction at relative -1PN and 2PN orders. Such modifications lead to corrections in the emitted gravitational waves that can be mapped to the parameterized post-Einsteinian framework. Such modifications could be strongly constrained with gravitational wave observations.Comment: 26 pages, 3 figures, 2 tables (matches version published in PRD); v3 prepended an erratu

Ontogeny of Hemidactylus (Gekkota, Squamata) with emphasis on the limbs

Squamate reptiles constitute a major component of the world's terrestrial vertebrate diversity, encompassing many morphotypes related to ecological specialization. Specifically, Gekkota, the sister clade to most other squamates, have highly specialized autopodia, which have been linked to their ecological plasticity. In this study, a developmental staging table of the gecko Hemidactylus, housed at the Museum fur Naturkunde, is established. Twelve post-ovipositional stages are erected, monitoring morphological embryological transitions in eye, ear, nose, heart, limbs, pharyngeal arches, and skin structures. Ecomorphological specializations in the limbs include multiple paraphalanges, hypothesized to aid in supporting the strong muscles, that are situated adjacent to metacarpal and phalangeal heads. Furthermore, some phalanges are highly reduced in manual digits III and IV and pedal digits III, IV, and V. Development, composition, and growth of limb elements is characterized in detail via mu CT, histochemistry, and bone histological analysis. Using known life history data from two individuals, we found an average lamellar bone accretion rate in the humeral diaphysis comparable to that of varanids. Various adult individuals also showed moderate to extensive remodeling features in their long bone cortices, indicating that these animals experience a highly dynamic bone homeostasis during their growth, similar to some other medium-sized to large squamates. This study of in-ovo development of the gecko Hemidactylus and its ecomorphological specializations in the adult autopodia, enlarges our knowledge of morphological trait evolution and of limb diversity within the vertebrate phylum.Peer reviewe

Modulation of μ‐opioid receptor activation by acidic pH is dependent on ligand structure and an ionizable amino acid residue

Background and Purpose: Adverse side effects of conventional opioids can be avoided if ligands selectively activate peripheral opioid receptors in injured tissue. Injury and inflammation are typically accompanied by acidification. In this study, we examined influences of low pH and mutation of the ionizable amino acid residue H297(6.52) on mu-opioid receptor binding and signalling induced by the mu-opioid receptor ligands fentanyl, DAMGO, and naloxone. Experimental Approach: HEK 293 cells stably transfected with mu-opioid receptors were used to study opioid ligand binding, [S-35]-GTP gamma S binding, and cAMP reduction at physiological and acidic pH. We used mu-opioid receptors mutated at H297(6.52) to A (MOR-H297(6.52)A) to delineate ligand-specific interactions with H297(6.52). Key Results: Low pH and the mutant receptor MOR-H297(6.52)A impaired naloxone binding and antagonism of cAMP reduction. In addition, DAMGO binding and G-protein activation were decreased under these conditions. Fentanyl-induced signalling was not influenced by pH and largely independent of H297(6.52). Conclusions and Implications: Our investigations indicate that low pH selectively impairs mu-opioid receptor signalling modulated by ligands capable of forming hydrogen bonds with H297(6.52). We propose that protonation of H297(6.52) at acidic pH reduces binding and subsequent signalling of such ligands. Novel agonists targeting opioid receptors in injured tissue might benefit from lack of hydrogen bond formation with H297(6.52)

Création de storyboards dynamiques pour la visualisation d'animations

National audienceReprésenter un ensemble complexe de mouvements sous forme condensée, par exemple dans une image, est un problème qui se pose dans de nombreux domaines, allant de la visualisation scientifique à la conception de story-boards ou de bandes dessinées. Une image (espace à deux dimensions) ne peut représenter le mouvement de particules dans l'espace (données 4D) sans perte d'information. Pour compenser cette perte, plusieurs techniques ont été développées, allant de l'ajout d'indices visuels dans une image au découpage du mouvement en une séquence de plusieurs images. Dans cet article, nous présentons un pipeline pour générer, à partir de données correspondant à un ensemble de mouvements dans l'espace et sur une certaine durée temporelle, un storyboard résumant de manière compréhensible et efficace l'ensemble de l'animation. Notre méthode consiste à grouper les données ayant un mouvement similaire, puis à segmenter ces groupes pour isoler des positions clefs. Enfin, nous effectuons un rendu stylisé de la trajectoire correspondant à chaque segment. L'objectif de notre travail est de permettre une exploration dynamique du storyboard obtenu, de telle sorte qu'un utilisateur puisse observer les données à plusieurs échelles, aussi bien spatiales que temporelles. Voir aussi http://artis.imag.fr/Publications/2008/SH0

The use of classification and regression trees to predict the likelihood of seasonal influenza

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therap

Applying QNLP to sentiment analysis in finance

As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly advancing field of Quantum Natural Language Processing (QNLP), we explore the practical applicability of the two central approaches DisCoCat and Quantum-Enhanced Long Short-Term Memory (QLSTM) to the problem of sentiment analysis in finance. Utilizing a novel ChatGPT-based data generation approach, we conduct a case study with more than 1000 realistic sentences and find that QLSTMs can be trained substantially faster than DisCoCat while also achieving close to classical results for their available software implementations