Topological data analysis of human vowels: Persistent homologies across representation spaces

Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The standard TDA pipeline starts from the raw signal data or a representation of it. Then, it consists in building a multiscale topological structure on the top of the data using a pre-specified filtration, and finally to compute the topological signature to be further exploited. The commonly used topological signature is a persistent diagram (or transformations of it). Current research discusses the consequences of the many ways to exploit topological signatures, much less often the choice of the filtration, but to the best of our knowledge, the choice of the representation of a signal has not been the subject of any study yet. This paper attempts to provide some answers on the latter problem. To this end, we collected real audio data and built a comparative study to assess the quality of the discriminant information of the topological signatures extracted from three different representation spaces. Each audio signal is represented as i) an embedding of observed data in a higher dimensional space using Taken's representation, ii) a spectrogram viewed as a surface in a 3D ambient space, iii) the set of spectrogram's zeroes. From vowel audio recordings, we use topological signature for three prediction problems: speaker gender, vowel type, and individual. We show that topologically-augmented random forest improves the Out-of-Bag Error (OOB) over solely based Mel-Frequency Cepstral Coefficients (MFCC) for the last two problems. Our results also suggest that the topological information extracted from different signal representations is complementary, and that spectrogram's zeros offers the best improvement for gender prediction

Detecting human and non-human vocal productions in large scale audio recordings

We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings. Through a series of computational steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network for detecting various types of natural vocal productions in a noisy data stream without requiring a large sample of labeled data. We test it on two different data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording

A Modeling Approach towards Identifying Potential Bivalent Sensitizers of Neuromuscular Blocking Agents

ABSTRACT Objective: Anaphylactic reactions induced by neuromuscular blocking agents (NMBAs) can occur at first contact and might be due to cross-sensitization by other drugs or chemicals. Our aim was to investigate whether divalent molecules sharing chemical features with NMBAs might potentially cause cross-sensitization. Methods: We constructed a pharmacophore key from chemical features common to all NMBAs (two positive or ionizable features 1.0807 nm apart) and used the key to screen FDA-approved small drug molecules of the Drug Bank® database (1541 molecules). The selected molecules were categorized on the basis of the values for three main parameters (fit value, relative energy and mean polar surface area). Results: Screening from the pharmacophore key selected 13 NMBAs and 88 non-NMBA drugs. Of these 88 drugs, 42 had high-ranking parameter values and were considered preferential cross-sensitizers. These included the dopamine D2 receptor ligands aripiprazole and domperidone. Pholcodine, as well as nizatidine, ranitidine, antrafenine, cabergoline and, to some extent, chlorhexidine best fulfilled the required criteria of apolar character, bioavailability and ionization rate. Conclusion: Our data support the hypothesis that pholcodine might be a potential NMBA cross-sensitizer. They confirmed the results of inhibition tests on patient serum suggesting that dopamine D2 receptor ligands might be cross-sensitizers. They also identified chlorhexidine, a widely used disinfectant incriminated in several cases of immediate hypersensitivity reactions, as a potential cross-sensitizer. Pharmacophore modelling is an inexpensive, straightforward approach that can be used to identify potential NMBA cross-sensitizing agents

Colonization history of the western corn rootworm (Diabrotica virgifera virgifera) in North America: insights from random forest ABC using microsatellite data

First described from western Kansas, USA, the western corn rootworm, Diabrotica virgifera virgifera, is one of the worst pests of maize. The species is generally thought to be of Mexican origin and to have incidentally followed the expansion of maize cultivation into North America thousands of years ago. However, this hypothesis has never been investigated formally. In this study, the genetic variability of samples collected throughout North America was analysed at 13 microsatellite marker loci to explore precisely the population genetic structure and colonization history of D. v. virgifera. In particular, we used up-to-date approximate Bayesian computation methods based on random forest algorithms to test a Mexican versus a central-USA origin of the species, and to compare various possible timings of colonization. This analysis provided strong evidence that the origin of D. v. virgifera was southern (Mexico, or even further south). Surprisingly, we also found that the expansion of the species north of its origin was recent—probably not before 1100 years ago—thus indicating it was not directly associated with the early history of maize expansion out of Mexico, a far more ancient event

Approches radicalaires en cascade vers le système polycyclique des alcaloïdes cytotoxiques de la perophoramidine et des communésines

REIMS-BU Santé (514542104) / SudocSudocFranceF

Le couplage de Suzuki dans la synthèse d'hétérocycles azotés d'intérêt thérapeutique

REIMS-BU Santé (514542104) / SudocSudocFranceF

Detection and classification of vocal productions in large scale audio recordings

We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings and classify these vocal productions. The pipeline is based on a deep neural network and adresses both issues simultaneously. Though a series of computationel steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network without requiring a large sample of labeled data and important computing resources. Our end-to-end methodology can handle noisy recordings made under different recording conditions. We test it on two different natural audio data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording

Cinnamide Derivatives as Mammalian Arginase Inhibitors: Synthesis, Biological Evaluation and Molecular Docking

Arginases are enzymes that are involved in many human diseases and have been targeted for new treatments. Here a series of cinnamides was designed, synthesized and evaluated in vitro and in silico for their inhibitory activity against mammalian arginase. Using a microassay on purified liver bovine arginase (b-ARG I), (E)-N-(2-phenylethyl)-3,4-dihydroxycinnamide, also named caffeic acid phenylamide (CAPA), was shown to be slightly more active than our natural reference inhibitor, chlorogenic acid (IC50 = 6.9 ± 1.3 and 10.6 ± 1.6 µM, respectively) but it remained less active that the synthetic reference inhibitor Nω-hydroxy-nor-l-arginine nor-NOHA (IC50 = 1.7 ± 0.2 µM). Enzyme kinetic studies showed that CAPA was a competitive inhibitor of arginase with Ki = 5.5 ± 1 µM. Whereas the activity of nor-NOHA was retained (IC50 = 5.7 ± 0.6 µM) using a human recombinant arginase I (h-ARG I), CAPA showed poorer activity (IC50 = 60.3 ± 7.8 µM). However, our study revealed that the cinnamoyl moiety and catechol function were important for inhibitory activity. Docking results on h-ARG I demonstrated that the caffeoyl moiety could penetrate into the active-site pocket of the enzyme, and the catechol function might interact with the cofactor Mn2+ and several crucial amino acid residues involved in the hydrolysis mechanism of arginase. The results of this study suggest that 3,4-dihydroxycinnamides are worth being considered as potential mammalian arginase inhibitors, and could be useful for further research on the development of new arginase inhibitors

Nanovectorization of TRAIL with single wall carbon nanotubes enhances tumor cell killing

International audienceTumor necrosis factor-related apoptosis-inducing ligand (TRAIL or Apo2L) is a member of the tumor necrosis factor (TNF) superfamily. This type II transmembrane protein is able to bound specifically to cancer cell receptors (i.e., TRAIL-R1 (or DR4) and TRAIL-R2 (or DR5)) and to induce apoptosis without being toxic for healthy cells. Because membrane-bound TRAIL induces stronger receptor aggregation and apoptosis than soluble TRAIL, we proposed here to vectorize TRAIL using single-walled carbon nanotubes (SWCNTs) to mimic membrane TRAIL. Owing to their exceptional and revolutional properties, carbon nanotubes, especially SWCNTs, are used in a wide range of physical or, now, medical applications. Indeed due to their high mechanical resistance, their high flexibility and their hydrophobicity, SWCNTs are known to rapidly diffuse in an aqueous medium such as blood, opening the way of development of new drug nanovectors (or nanocarriers). Our TRAIL-based SWCNTs nanovectors proved to be more efficient than TRAIL alone death receptors in triggering cancer cell killing. These NPTs increased TRAIL pro-apoptotic potential by nearly 20-fold in different Human tumor cell lines including colorectal, nonsmall cell lung cancer, or hepatocarcinomas. We provide thus a proof-of-concept that TRAIL nanovector derivatives based on SWCNT may be useful to future nanomedicine therapies