Miracle-dz/UniFAR-FTUs huggingface dataset:
Photodynamic inactivation of Acanthamoeba polyphaga with curcuminoids: an in vitro study

Acanthamoeba polyphaga are free-living amoebae that can be considered potentially pathogenic organisms by cause serious human infections, including keratitis and granulomatous amoebic encephalitis that usually results in death. Photodynamic inactivation (PDI) has been used for the biological control of microorganisms and can be promise in the control of Acanthamoeba infections. This study evaluated the in vitro effectiveness of PDI in A. polyphaga using curcuminoids salt as photosensitizer (PS) besides observing morphological changes caused by this PS in this organism, in confocal microscopy. A. polyphaga trophozoites were grown at 37°C in PYG medium for 48 to 72 hours. After, the trophozoites were incubated with PS solution during one hour and the samples were irradiated using light-emitting diodes at 460 nm at light doses 30 and 50 J/cm2. The results revealed reduction of 27.7%, 61.4% and 82.5% at 30 J/cm2 and 75.2%, 85.0% and 95.9% at 50 J/cm2, respectively, at curcuminoid salt concentrations of 500, 1000 and 1500 μg/mL. Through fluorescence images, it was possible to visualize the curcuminoid salt’s uptake by the trophozoites. The PS showed toxicity to amoebae, in the dark, but the irradiation in PDI contributed to amoebae death effect. These data suggest that PDI may be an application of therapeutic intervention against Acanthamoeba infections, since it was effective in the inactivation of these amoebae.
Changes in unemployment and permanent sickness in England’s East Midlands coalfields, 1971–2011

ABSTRACT The UK coal industry was an important part of the economy, but it experienced substantial job losses during the 1980s and 1990s. To alleviate the resulting socio-economic problems, many areas received regeneration funds. We examine to what extent relative unemployment and permanent sickness and disability in the coalfields of the East Midlands changed between 1971 and 2011. Over this period rates of permanent sickness and disability have increased, and the gap widened between coalfields and non-coalfields. In contrast, unemployment has decreased at a faster rate in the coalfields. However, both outcomes are better in those coalfields that were less dependent on mining.
Stability of Traveling Wave Fronts for Nonlocal Diffusion Equation with Delayed Nonlocal Response

In this paper, we consider with the stability of traveling wave fronts for the nonlocal diffusion equation with delay and global response. We first establish the existence and comparison theorem of solutions for the nonlocal reaction-diffusion equation by appealing to the theory of abstract functional differential equation. Then we further show that the traveling wave fronts are asymptotical stability with phase shift. Our main technique is the super and subsolution method coupled with the comparison principle and squeezing method.
A Conformal Leaky-Wave Antenna Design for IoMT-Based WBANs

Continuous health monitoring of vital signs of patients is a challenging issue, especially in emergency medical conditions. This paper designs a practical internet of medical things-based wireless body area network (IoMT-based WBAN), to address this issue. Accordingly, a two-fold test-bed design is proposed taking $i$ ) signaling and $ii$ ) antenna configuration into account to attain uninterruptible on/off-body communication links. Firstly, the Walsh-Hadamard coding technique is used in all bio-sensors to retain orthogonal simultaneous signaling for on-body links. Secondly, an antenna configuration of the hub is designed so that it prevents probable interruptions in off-body links which may be caused by some human postures. More accurately, a novel periodic leaky-wave antenna (LWA) with an elliptical belt shape is introduced which generates a quasi-omnidirectional pattern. The LWA is designed based on a multi-tone periodicity of a width-modulated microstrip line. At the design frequency of 5.8 GHz, the suggested conformal periodic LWA was simulated and then fabricated. Simulations and measurement results illustrate that the performance of on/off-body communication links is improved in comparison to conventional antennas. Furthermore, simulated and measured radiation patterns have a good agreement with theoretical calculations. Moreover, specific absorption rate (SAR) values of the proposed antenna are significantly below the SAR limits so that this technique can be highly recommended for WBAN applications.
The French Cohort of DNA Repair-Deficient Xeroderma Pigmentosum Patients: Risk of Hematological Malignancies

Simple Summary Xeroderma pigmentosum (XP) is a genetic disease caused by DNA repair deficiency. We diagnosed 181 XP patients from 1982 to 2022 in France. The patients are very sensitive to sunlight and will rapidly develop skin cancers in exposed body sites. However, thanks to better protection, XP patients live much longer than in the past. This better survival is associated, for the French XP patients originating from North Africa and bearing the same founder mutation in the XPC gene, with a very high risk to develop aggressive and lethal internal cancers, particularly hematological malignancies, and brain, gynecological, and thyroid tumors. This is a new threat hanging over all XP-C patients. The molecular origins of internal tumors in this particular set of DNA repair-deficient patients are discussed. It is important that physicians and XP families be aware of this high risk to prevent and look for early diagnosis of internal tumor development in XP patients. Abstract Background: Xeroderma pigmentosum (XP) is a rare genetic disorder characterized by a high incidence of skin cancers. These patients are deficient in nucleotide excision repair caused by mutations in one of the 7 XP genes. Methods: We diagnosed 181 XP patients using UV-induced DNA repair measurements and/or DNA sequencing from 1982 to 2022 in France. Results: As all XP patients, the French ones are very sensitive to UV exposure but since they are usually very well protected, they develop relatively few skin cancers. A majority of French XP patients originate from North Africa and bear a founder mutation on the XPC gene. The striking discovery is that these patients are at a very high risk to develop aggressive and lethal internal tumors such as hematological malignancies (more than a 100-fold risk compared to the general population for myelodysplasia/leukemia) with a median age of death of 25 years, and brain, gynecological, and thyroid tumors with even lower median ages of death. The high mutation rates found in XP-C internal tumors allow us to think that these XP patients could be successfully treated by immunotherapies. A full analysis of the molecular origins of these DNA repair-deficient tumors is discussed. Several explanations for this high predisposition risk are proposed. Conclusions: As the age of the XP population is increasing due to better photo-protection, the risk of lethal internal tumors is a new Damocles sword that hangs over XP-C patients. This review of the French cohort is of particular importance for alerting physicians and families to the prevention and early detection of aggressive internal tumors in XP patients.
Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)‐based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three‐layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three‐layer composite generally provided lower overall losses for the training samples, a higher F1‐score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN‐based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN‐based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three‐layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.
Multiplexed Gene Engineering Based on dCas9 and gRNA-tRNA Array Encoded on Single Transcript

Simultaneously, multiplexed genome engineering and targeting multiple genomic loci are valuable to elucidating gene interactions and characterizing genetic networks that affect phenotypes. Here, we developed a general CRISPR-based platform to perform four functions and target multiple genome loci encoded in a single transcript. To establish multiple functions for multiple loci targets, we fused four RNA hairpins, MS2, PP7, com and boxB, to stem–loops of gRNA (guide RNA) scaffolds, separately. The RNA-hairpin-binding domains MCP, PCP, Com and λN22 were fused with different functional effectors. These paired combinations of cognate-RNA hairpins and RNA-binding proteins generated the simultaneous, independent regulation of multiple target genes. To ensure that all proteins and RNAs are expressed in one transcript, multiple gRNAs were constructed in a tandemly arrayed tRNA (transfer RNA)-gRNA architecture, and the triplex sequence was cloned between the protein-coding sequences and the tRNA-gRNA array. By leveraging this system, we illustrate the transcriptional activation, transcriptional repression, DNA methylation and DNA demethylation of endogenous targets using up to 16 individual CRISPR gRNAs delivered on a single transcript. This system provides a powerful platform to investigate synthetic biology questions and engineer complex-phenotype medical applications.
DC-DFFN: Densely Connected Deep Feature Fusion Network With Sign Agnostic Learning for Implicit Shape Representation

Reconstructing 3D surfaces from raw point cloud data is still a challenging and complex problem in computer vision and graphics. Recently emerged neural implicit representations model 3D surfaces implicitly in arbitrary resolution and diverse topologies. In this domain, most of the studies have so far used a single latent code-based variational auto-encoder (VAE) or auto-decoder (AD) architectures, or architectures similar to UNets. Due to the deep architectures of the existing approaches, gradients and/or input information can vanish while passing through the layers, which can cause suboptimal learning at training time and consequently low performance at test time. As a countermeasure, skip connections and feature fusion have been used in related application fields of convolutional neural networks. In this study, we embrace this idea and propose a novel densely connected deep feature fusion network, DC-DFFN, architecture for implicit shape representation. In the experimental results we show that DC-DFFN outperforms baseline approaches in terms visual reconstruction quality and quantitatively based on several measures. In addition, the proposed approach provides faster convergence during training compared to the baseline approaches. The DC-DFFN architecture has been implemented in PyTorch and is available as open source.
Large Eddy Simulation of Non-premixed Turbulent Combustion Using Nanosecond Pulsed Actuators

We present a numerical study of non-premixed turbulent combustion using nanosecondpulse discharges for stabilization. Both k- turbulence model and large eddy simulation are considered to investigate the non-premixed turbulent combustion of methane-air flow. The benchmark case is compared with other simulation and experiment in good agreement. For the case of plasma assisted combustion, nanosecond-pulse discharges are able to create acoustic pressure waves within a few microseconds. Induced acoustic waves may enhance chemical reactions for flame stabilization. The instantaneous result shows that nanosecondpulse discharges result in modification of the flame shape.
CALCUL DES COEFFICIENTS ET DU FILTRE DE RE - ECHANTILLONNAGE D'IMAGES NUMERIQUES DE LA B - SPLINE CUBIQUE UNIFORME

Dans le cas du re-echantillonnage d'images numeriques par la B-spline cubique uniforme, un pixel de sortie est calcule par un filtre applique sur 16 pixels voisins (pv) d’une image ou d’une matrice de coefficients C. Pour obtenir cette matrice C, nous devons resoudre un systeme d'equations lineaires. Nous resolvons ce systeme lineaire par une factorisation de Cholesky adaptee. Cette technique nous a permis de calculer la matrice C avec un temps de calcul inferieur a celui du filtre des 16 pixels voisins. Nous presentons, dans cet article, l'algorithme de resolution, le filtre de re-echantillonnage, les complexites et les temps de calcul pour son implementation. Le calcul des complexites et les essais numeriques ont montre que le cout de l'algorithme est O(n 2 ). t6 x���x��'font-size:9.0pt; font-family:"Arial","sans-serif";font-style:normal'>M. MAHBOUB 1
Mathematical Methods for an Accurate Navigation of the Robotic Telescopes

Accurate sky identification is one of the most important functions of an automated telescope mount. The more accurately the robotic telescope is navigated to the investigated part of the sky, the better the observations and discoveries made. In this paper, we present mathematical methods for accurate sky identification (celestial coordinates determination). They include the automatic selection of the reference stars, preliminary and full sky identification, as well as an interaction with international databases, which are a part of the astrometric calibration. All described methods help to receive accurately calculated astrometric data and use it for the positional calibration and better navigation of the automated telescope mount. The developed methods were successfully implemented in the Collection Light Technology (CoLiTec) software. Through its use, more than 1600 small solar system objects were discovered. It has been used in more than 700,000 observations and successful sky identifications, during which, five comets were discovered. Additionally, the accuracy indicators of the processing results of the CoLiTec software are provided in the paper, which shows benefits of the CoLiTec software and lower standard deviation of the sky identification in the case of low signal-to-noise ratios.
Nonlinear Vibrations Of Axisymmetric CircularPlates With Variable Thickness

The BEM is developed for nonlinear vibration analysis of axisymmetric circular plates with variable thickness undergoing large deflections. General boundary conditions are considered which may be also nonlinear. The problem is formulated in terms of displacements. The solution is based on the concept of the analog equation, according to which the two coupled nonlinear differential equations with variable coefficients, pertaining to the inplane radial and transverse deformation, are converted to two uncoupled linear ones of a substitute beam with unit axial and unit bending stiffness, respectively, under fictitious quasi-static load distributions. Numerical examples are presented, which illustrate the method and demonstrate its efficiency and accuracy.
An Experimental Study on Optimization in Permutation Spaces by Quantum-Inspired Evolutionary Algorithm Using Quantum Bit Representation

The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS), where each gene is represented by a quantum bit (qubit) in both algorithms, have shown superior search performance to the classical genetic algorithm in the 0-1 knapsack problem. Also, from experimental results for the integer knapsack problem, a novel integer-type gene-coding method that can obtain an integer value as an observation result by assigning multiple qubits in a gene locus has shown superior search performance to the conventional binary-type gene-coding method. However, the integer-type gene-coding method cannot deal with permutations simply. Therefore, we have proposed two interpretation methods that can deal with permutations in order to expand the gene-coding method based on the qubit representation. From the results of a computer experiment using the proposed interpretation methods in the traveling salesman problem, we have clariﬁed that the two proposed interpretation methods can search for the optimal solution, even with the gene-coding method based on the qubit representation. Moreover, there are suitable rotation angles for discovering the optimal solution that depend on the algorithm.
Numerical methods for wave equation in heterogenous media

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Soft Lightweight Small-Scale Parallel Robot With High-Precision Positioning

Small-scale (from centimeters down to micrometers) parallel robots with high precision are widely utilized in various industrial and biomedical settings, while such superiorities remain challenges for soft parallel robots (SPRs). In this work, we propose an integrated design and fabrication strategy to make up a soft lightweight (3.5 g) small-scale (<inline-formula><tex-math notation="LaTeX">$\phi 60\times$</tex-math></inline-formula>40 mm) parallel robot based on the dielectric elastomer actuator. Then, a hybrid model is established to describe the mapping between driving space and workspace, taking advantage of the robustness and security of the model-based method and the strong nonlinear fitting ability of the data-driven neural network method. The stiffness and workspace of the robot are analyzed. The results of trajectory tracking experiments demonstrate the accuracy and robustness of the proposed hybrid model. The average positioning error of the different trajectories is 13.4–16.6<inline-formula><tex-math notation="LaTeX">$\,\mu\mathrm{m}$</tex-math></inline-formula>. Finally, the zebrafish embryo puncture experiment is carried out to show the ability of micromanipulation. This research paves a new avenue for designing and controlling high-positioning SPRs, which is expected to be applied in the micromanipulation field.
Segmentation of Human Body Parts Using Deformable Triangulation

This paper presents a new segmentation algorithm to segment a body posture into different body parts using the technique of triangulation. For well analyzing each posture, we first propose a triangulation-based method to triangulate it to different triangle meshes. Then, we use a depth-first search scheme to find a spanning tree as its skeleton feature from the set of triangulation meshes. The triangulation-based scheme to extract important skeleton features has more robustness and effectiveness than other silhouette-based approaches. Then, different body parts can be roughly extracted by removing all the branching points from the spanning tree. A model-driven technique is then proposed for more accurately segmenting a human body into semantic parts. This technique uses the concept of Gaussian mixture model (GMM) to model different visual properties of different body parts. Then, a suitable segmentation scheme can be driven by classifying these models using their skeletons. Experimental results have proved that the proposed method is robust, accurate, and powerful in body part segmentation