gebhart/patrepeval huggingface dataset:
A Dynamic Pruning and Feature Selection Strategy for Real-Time Tracking (2005)

Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output of a collection of video trackers, each attending to a different feature or cue on the target. We show both theoretically and experimentally that the method used to prune the growth of target hypotheses can have a great impact on the trackers performance, and indirectly, change the benefit of using linear score combination as opposed to a non-linear rank combination for fusion. We also show that the rank-score graph defined by Hsu and Taksa can be used to select a subset of features to fuse to reduce classification error.
Gigabit Mobile Communications Using Real-Time MIMO-OFDM Signal Processing (2018)

A modern approach for wireless fading channels is to use multiple antennas both at the Tx and Rx in combination with sophisticated space–time signal processing techniques, as space–division multiple access (SDMA) and multiple-input multiple-output (MIMO). The well-known SDMA and MIMO algorithms for frequency-flat fading channels can therefore be used also in frequency-selective channels, by applying them individually on each orthogonal frequency division multiplexing (OFDM) subcarrier. Most MIMO-OFDM prototypes in the recent literature transmit the signals over the air and perform the processing off-line. There is an ever-increasing demand for higher bandwidths in mobile communication systems. While the target of the first generations was mobile telephony, deployed systems aim to enhance people’s lives by enabling high-speed mobile access to the Internet anywhere and at any time. In a mobile communications environment, the base station often has sector antennas and knows the direction of the terminal equipped with omnidirectional antennas only coarsely.
De novo identification of toxicants that cause irreparable damage to parasitic nematode intestinal cells (2019)

Abstract Efforts to identify new drugs for therapeutic and preventive treatments against parasitic nematodes have gained increasing interest with expanding pathogen omics databases and drug databases from which new anthelmintic compounds might be identified. Here, a novel approach focused on integrating a pan-Nematoda multi-omics data targeted to a specific nematode organ system (the intestinal tract) with evidence-based filtering and chemogenomic screening was undertaken. Based on de novo computational target prioritization of the 3,564 conserved intestine genes in A. suum , exocytosis was identified as a high priority pathway, and predicted inhibitors of exocytosis were tested using the large roundworm ( Ascaris suum larval stages), a filarial worm ( Brugia pahangi adult and L3), a whipworm ( Trichuris muris adult), and the non-parasitic nematode Caenorhabditis elegans . 10 of 13 inhibitors were found to cause rapid immotility in A. suum L3 larvae, and five inhibitors were effective against the three phylogenetically diverse parasitic nematode species, indicating potential for a broad spectrum anthelmintics. Several distinct pathologic phenotypes were resolved related to molting, motility, or intestinal cell and tissue damage using conventional and novel histologic methods. Pathologic profiles characteristic for each inhibitor will guide future research to uncover mechanisms of the anthelmintic effects and improve on drug designs. This progress firmly validates the focus on intestinal cell biology as a useful resource to develop novel anthelmintic strategies. Author summary The intestinal cells of parasitic nematodes are not known to regenerate, therefore disruption of essential processes that cause irreparable damage to intestinal cells is expected to promote worm expulsion. To facilitate improved methods of therapy we need to better understand the basic intestinal cell and tissue functions of this critical organ. To that end have undertaken a comprehensive analysis of multi-omics omics data and identify and prioritize intestinal genes/pathways with essential functions and associated drugs and established a foundational model of the STH intestinal system using the large roundworm Ascaris suum to test and validate inhibitors of these functions. We found 10 inhibitors to impacted motility, and seven of those showed severe pathology and an apparent irreparable damage to intestinal cells. Furthermore, five inhibitors were effective against the three phylogenetically diverse parasitic nematode species, indicating potential for a broad spectrum anthelmintics. Our results firmly validate the focus on intestinal cell biology as a useful resource to develop novel anthelmintic strategies.
Facile Synthesis of Amphiphilic Fluorescent Phosphorus Dendron-Based Micelles as Antiproliferative Agents: First Investigations (2021)

We designed and synthesized several families of novel amphiphilic fluorescent phosphorus dendron-based micelles showing relevant antiproliferative activities for use in the field of theranostic nanomedicine. Based on straightforward synthesis pathways, 12 amphiphilic phosphorus dendrons bearing 10 protonated cyclic amino groups (generation one), or 20 protonated amino groups (generation two), and 1 hydrophobic chain carrying 1 fluorophore moiety were created. The amphiphilic dendron micelles had the capacity to aggregate in solution using hydrophilic/hydrophobic interactions, which promoted the formation of polymeric micelles. These dendron-based micelles showed moderate to high antiproliferative activities against a panel of tumor cell lines. This paper presents for the first time the synthesis and our first investigations of new phosphorus dendron-based micelles for cancer therapy applications.
S-ADDOPT: Decentralized Stochastic First-Order Optimization Over Directed Graphs (2020)

In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes. In contrast to the existing work, we use gradient tracking to improve certain aspects of the resulting algorithm. In particular, we propose the S-ADDOPT algorithm that assumes a stochastic first-order oracle at each node and show that for a constant step-size α, each node converges linearly inside an error ball around the optimal solution, the size of which is controlled by α. For decaying step-sizes O(1/k), we show that S-ADDOPT reaches the exact solution sublinearly at O(1/k) and its convergence is asymptotically network independent. Thus the asymptotic behavior of S-ADDOPT is comparable to the centralized stochastic gradient descent. Numerical experiments over both strongly convex and nonconvex problems illustrate the convergence behavior and the performance comparison of the proposed algorithm.
Constrained image restoration applied to passive millimeter-wave images (2004)

Passive millimeter-wave imaging has excellent all weather capability but is severely diffraction limited and requires large apertures to give adequate spatial resolution. Linear restoration can enhance the resolution by a factor of two, while under favorable conditions non-linear restoration can enhance it by factors of four. The amount of enhancement possible is generally limited by the amount of noise present in the original observed image. Preprocessing can reduce the effect of this noise. In many non-linear restoration techniques the amount of high spatial frequency introduced into the restored image is uncontrolled. This problem has been overcome through the use of the Lorentzian algorithm, which imposes a statistical constraint on the distribution of gradients within the restored image. Another way of applying a constraint is to selectively restore an image. The high spatial frequency content of an image exists largely at edges and sharp features and needs to be restored, while the smoother background between features contains fewer high frequencies and needs less restoration. Adaptive non-linear restoration techniques have been investigated whereby the amount of restoration applied to an image is a function of the first and second derivative of the image intensity. Images are presented to demonstrate the effectiveness of these methods.
Numerical investigation of dual-frequency HIFU pulsing for lithotripsy (2003)

As an alternative to traditional shock-wave lithotripsy, high-intensity focused ultrasound (HIFU) is currently being investigated for its capability to comminute renal calculi. Because current data indicate that cavitation plays a role in both stone comminution as well as collateral tissue damage, the cavitation effects of HIFU treatment strategies are investigated numerically. In particular, numerical simulations are designed to model the response of bubbles to acoustic excitations generated by a prototype, dual-frequency HIFU transducer for lithotripsy. The prototype transducer is capable of producing both high- (∼4-MHz) and low-frequency (∼100-kHz) outputs, while the bubble dynamics are modeled by the Gilmore equation for a single spherical bubble subject to diffusion. Numerical simulations are currently ongoing to investigate the effects of the relative phase between high and low-frequency pulses. Initial results demonstrate that the simultaneous application of high and low-frequency pulses can generate maximum pressures several orders of magnitude higher than high-frequency pulses alone.
Lignin and lignin derivatives as components in biobased hot melt adhesives (2019)

ABSTRACT Hot melt adhesives (HMAs) are formulated for the first time with different lignins as major components, and the developed HMA formulations were tested for gluing paperboard. The best formulations showed equal or even better bond strength compared to a commercial HMA reference. A maximum bond strength of 16.1 N was achieved with a formulation of oxidized cellulose acetate, organosolv lignin, and triethyl citrate, whereas the bond strength of the commercial HMA reference was 10.5 N. The performance was adjusted via the selection of lignin, the formulation, and chemical modification. Lignin modification was not necessary but provided further possibilities for adjusting the properties for different products (reversible vs irreversible adhesive seams) and also for producing plasticizer‐free formulations. Modification with tall oil fatty acids enabled the formulation of fully biobased HMAs without any external plasticizer and provided a bond strength as high as high as 8.9 N. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47983.
Atypia predicting prognosis for intracranial extraventricular neurocytomas (2011)

There are at least 2 distinct histological subtypes of EVN, with different prognostic significances. Atypia or MIB-1 labeling index greater than 3% is a significant predictor of poor prognosis for EVNs. Complete resection or more aggressive attempts at providing adjuvant therapy following STR appear to improve the prognosis for patients with EVNs. Although the authors' results are informative, there are limitations to their analysis. Given the relatively modest total number of cases reported, as well as the nature of the disaggregated analysis, the authors were not able to use formal meta-analytical methods to limit the impact of between center heterogeneity. Additionally, they were not able to control for individual differences in data analysis and presentation across the different studies included in their analysis.
Real‐time intelligent classification of COVID ‐19 and thrombosis via massive image‐based analysis of platelet aggregates (2023)

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.
An optimization framework for combining multiple graphs (2016)

This paper introduces a novel framework for combining multiple weighted graphs into a single optimized weighted graph. In our framework, we first develop a statistical formulation for the graph combining problem with a maximum likelihood criterion, and derive its optimality conditions. We then use these conditions to formulate the deterministic graph combining problem and propose a solution. Our experimental results show that the proposed solution provides better modeling compared to the commonly used averaging method. The introduced framework has various applications in signal processing and machine learning.
Engineering Microorganisms for the Production of Pharmaceutical Biomolecules (2018)

Developments in the fields of metabolic engineering, systems biology, and synthetic biology have enabled the engineering of microbial cells to produce natural and the non-­natural biomolecules with special biological activities. Many of these compounds are natural products isolated originally in tiny amounts from plants, animal tissues, or microorganisms. Flavonoids are secondary metabolites widespread in the plant kingdom. These colorful natural products are low-molecular-weight molecules composed of a simple 15 carbon backbone. The flavonoids biosynthetic pathway in plants is well studied and was found to have a common core unit, chalcone, from the phenylpropanoid pathway. Other researchers have made use of modular metabolic engineering to enhance flavonoid production in microbial hosts. By this approach, the biosynthetic pathway is designed as a combination of a few modules, where each of them is composed of a subset of all the metabolic reactions.
Modelling of the hardwood harvesting process: feeding model (2013)

L'objectif des travaux présentés concerne la modélisation des procédés de bûcheronnage actuels et en cours de développement dans le cadre du projet ECOMEF (Éco-concevoir un outil de mécanisation pour le bûcheronnage dans les peuplements feuillus), et ce en vue d'évaluer et d'optimiser les performances (gain de productivité, mais également efficacité énergétique) des nouvelles solutions envisagées au regard des systèmes existants. Après une présentation du cadre de travail, cette publication présente, dans une première partie, une démarche de décomposition fonctionnelle basée sur la méthode SADT, en vue de déterminer des « briques élémentaires » de modélisation des systèmes d'abattage existants. Ainsi, l'objectif général « Modéliser le fonctionnement d'une tête d'abattage» (considéré comme la fonction A-0 de notre diagramme SADT) a été décomposé en quatre fonctions, très proches des phases de travail des machines actuelles, à savoir modéliser le serrage, l'entraînement, l'ébranchage, et le billonnage du tronc. Au vu de cette décomposition et des priorités formalisées au sein du projet, trois modèles ont été identifiés comme indispensables à développer : un modèle d'entraînement du tronc, un modèle de coupe adapté à l'ébranchage par choc ainsi qu'un modèle de contact entre le tronc et les différentes parties de la tête d'abattage. Dans une seconde partie, le modèle d'entraînement est détaillé : les études existantes ainsi que les objectifs de ce modèle sont présentés. Afin de développer chaque modèle, deux démarches sont menées simultanément : le développement d'une approche analytique et la mise en place de campagnes d'essais expérimentaux en vue du recalage et/ou de la caractérisation des modèles. L'approche théorique mise en place pour le modèle d'entraînement est détaillée, et enfin le développement d'un banc d'essai et les expérimentations prévues pour le recalage du modèle sont évoqués, avant de conclure sur les résultats attendus.
An Extensively Hydrolyzed Formula Supplemented with Two Human Milk Oligosaccharides Modifies the Fecal Microbiome and Metabolome in Infants with Cow’s Milk Protein Allergy (2023)

Cow's milk protein allergy (CMPA) is a prevalent food allergy among infants and young children. We conducted a randomized, multicenter intervention study involving 194 non-breastfed infants with CMPA until 12 months of age (clinical trial registration: NCT03085134). One exploratory objective was to assess the effects of a whey-based extensively hydrolyzed formula (EHF) supplemented with 2'-fucosyllactose (2'-FL) and lacto-<i>N</i>-neotetraose (LNnT) on the fecal microbiome and metabolome in this population. Thus, fecal samples were collected at baseline, 1 and 3 months from enrollment, as well as at 12 months of age. Human milk oligosaccharides (HMO) supplementation led to the enrichment of bifidobacteria in the gut microbiome and delayed the shift of the microbiome composition toward an adult-like pattern. We identified specific HMO-mediated changes in fecal amino acid degradation and bile acid conjugation, particularly in infants commencing the HMO-supplemented formula before the age of three months. Thus, HMO supplementation partially corrected the dysbiosis commonly observed in infants with CMPA. Further investigation is necessary to determine the clinical significance of these findings in terms of a reduced incidence of respiratory infections and other potential health benefits.
Morphological, biological, biochemical, and karyotypic characteristics of human pancreatic ductal adenocarcinoma Capan-2 in tissue culture and the nude mouse. (1986)

Human pancreatic ductal adenocarcinoma Capan-2, derived from a 56-yr-old male Caucasian, has been studied in both tissue culture and the nude mouse. In tissue culture, tumor cells showed epithelial-like features, whereas in the nude mouse, the tumor grew as a well-differentiated adenocarcinoma, resembling histopathologically the original neoplasm. Ultrastructurally, the neoplastic cells showed characteristics of ductal epithelium. The allozyme phenotypic profile of Tumor Capan-2 was determined in eight genetically determined loci, and chromosome studies showed a hypotetraploid pattern with a number of morphological and numerical changes. Carcinoembryonic antigen was produced in trace amounts, and lactate dehydrogenase was represented only by Isoenzyme 5, regardless of environmental conditions. The characteristics of Capan-2 tumor make it a valuable addition to the small number of available pancreatic tumor lines in studies aiming at clarifying certain aspects of the biology of this type of malignancy.
Continued increases in hypertension over three decades in Fiji, and the influence of obesity (2015)

Significant increases have occurred in hypertension prevalence and SBP/DBP in both sexes and ethnicities of Fiji during 1980-2011 with no indication of decline, contributing to significant premature mortality from cardiovascular disease.
Lowering the transmission and spread of human coronavirus (2020)

The emergence of the severe acute respiratory syndrome coronavirus 2 pandemic has created an unprecedented healthcare, social, and economic disaster. Wearing of masks and social distancing can significantly decrease transmission and spread, however, due to circumstances such as medical or dental intervention and personal choice these practices have not been universally adopted. Additional strategies are required to lessen transmission. Nasal rinses and mouthwashes, which directly impact the major sites of reception and transmission of human coronaviruses (HCoV), may provide an additional level of protection against the virus. Common over-the-counter nasal rinses and mouthwashes/gargles were tested for their ability to inactivate high concentrations of HCoV using contact times of 30 s, 1 min, and 2 min. Reductions in titers were measured by using the tissue culture infectious dose 50 (TCID<sub>50</sub> ) assay. A 1% baby shampoo nasal rinse solution inactivated HCoV greater than 99.9% with a 2-min contact time. Several over-the-counter mouthwash/gargle products including Listerine and Listerine-like products were highly effective at inactivating infectious virus with greater than 99.9% even with a 30-s contact time. In the current manuscript we have demonstrated that several commonly available healthcare products have significant virucidal properties with respect to HCoV.
Réalisation et test d'un moteur supraconducteur à flux axial incluant un accouplement supraconducteur pour la propulsion navale (2015)

National audience
Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extended metabarcodes (2021)

ABSTRACT Background The increased usage of error-prone long-read sequencing for metabarcoding of fungi has not been matched with adequate public databases and concomitant analysis approaches. We address this gap and present a proof-of-concept study for classifying fungal taxa using linked machine learning classifiers. We demonstrate the capability of linked machine learning classifiers to accurately classify species and strains using real-world and simulated fungal ribosomal DNA datasets, including plant and human pathogens. We benchmark our new approach in comparison to current alignment and k-mer based methods based on synthetic mock communities. We also assess real world applications of species identification in complex unlabelled datasets. Results Our machine learning approach assigned individual nanopore long-read amplicon sequences to fungal species with high recall rates and low false positive rates. Importantly, our approach successfully distinguished between closely-related species and strains when individual read errors were higher than the genetic distance between individual taxa, which the alignment and k-mer methods could not do. The machine learning approach showed an ability to identify key species with high recall rates, even in complex samples of unknown species composition. Conclusions A proof of concept machine learning approach using a tree-descent approach on a decision tree of classifiers can identify known taxa with high accuracy, and precisely detect known target species from complex samples with high recall rates. We propose this approach is suitable for detecting the known knowns of pathogens or invasive species in any environment of mostly unknown composition, including agriculture and wild ecosystems.