Point Cloud Quality Assessment Using a One-Dimensional Model Based on the Convolutional Neural Network.

Recent advancements in 3D modeling have revolutionized various fields, including virtual reality, computer-aided diagnosis, and architectural design, emphasizing the importance of accurate quality assessment for 3D point clouds. As these models undergo operations such as simplification and compression, introducing distortions can significantly impact their visual quality. There is a growing need for reliable and efficient objective quality evaluation methods to address this challenge. In this context, this paper introduces a novel methodology to assess the quality of 3D point clouds using a deep learning-based no-reference (NR) method. First, it extracts geometric and perceptual attributes from distorted point clouds and represent them as a set of 1D vectors. Then, transfer learning is applied to obtain high-level features using a 1D convolutional neural network (1D CNN) adapted from 2D CNN models through weight conversion from ImageNet. Finally, quality scores are predicted through regression utilizing fully connected layers. The effectiveness of the proposed approach is evaluated across diverse datasets, including the Colored Point Cloud Quality Assessment Database (SJTU_PCQA), the Waterloo Point Cloud Assessment Database (WPC), and the Colored Point Cloud Quality Assessment Database featured at ICIP2020. The outcomes reveal superior performance compared to several competing methodologies, as evidenced by enhanced correlation with average opinion scores.

Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model.

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems.

A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection.

Autism spectrum disorder (ASD) represents an ongoing obstacle facing many researchers to achieving early diagnosis with high accuracy. To advance developments in ASD detection, the corroboration of findings presented in the existing body of autism-based literature is of high importance. Previous works put forward theories of under- and over-connectivity deficits in the autistic brain. An elimination approach based on methods that are theoretically comparable to the aforementioned theories proved the existence of these deficits. Therefore, in this paper, we propose a framework that takes into account the properties of under- and over-connectivity in the autistic brain using an enhancement approach coupled with deep learning through convolutional neural networks (CNN). In this approach, image-alike connectivity matrices are created, and then connections related to connectivity alterations are enhanced. The overall objective is the facilitation of early diagnosis of this disorder. After conducting tests using information from the large multi-site Autism Brain Imaging Data Exchange (ABIDE I) dataset, the results show that this approach provides an accurate prediction value reaching up to 96%.

A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images.

Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic burden. Deep Learning approaches based on Convolutional Neural Networks (CNNs) achieved remarkable improvements in knee OA detection. Despite this success, the problem of early knee OA diagnosis from plain radiographs remains a challenging task. This is due to the high similarity between the X-ray images of OA and non-OA subjects and the disappearance of texture information regarding bone microarchitecture changes in the top layers during the learning process of the CNN models. To address these issues, we propose a Discriminative Shape-Texture Convolutional Neural Network (DST-CNN), which automatically diagnoses early knee OA from X-ray images. The proposed model incorporates a discriminative loss to improve class separability and deal with high inter-class similarities. In addition, a new Gram Matrix Descriptor (GMD) block is embedded in the CNN architecture to compute texture features from several intermediate layers and combine them with the shape features in the top layers. We show that merging texture features with deep ones leads to better prediction of the early stages of OA. Comprehensive experimental results on two large public databases, Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) demonstrate the potential of the proposed network. Ablation studies and visualizations are provided for a detailed understanding of our proposed approach.

Diversity and Antimicrobial Activities of Actinobacteria Isolated from Mining Soils in Midelt Region, Morocco.

Multidrug-resistant bacteria have emerged as a serious global health threat that requires, more than ever before, an urgent need for novel and more effective drugs. In this regard, the present study sheds light on the diversity and antimicrobial potential of Actinobacteria isolates in mining ecosystems. We have indeed investigated the production of bioactive molecules by the Actinobacteria isolated from abandoned mining areas in Midelt, Morocco, where average contents of lead (Pb) and cadmium (Cd) are higher than normal world levels. One hundred and forty-five Actinobacteria isolates were isolated and characterized based on morphological, chemotaxonomical, biochemical, and molecular data. Most of the 145 isolates were identified as Streptomyces. Isolates affiliated to the genera Amycolatopsis, Lentzea, Actinopolymorpha, and Pseudonocardia were also found. Antimicrobial producing potentials of Actinobacteria isolates were assessed against eight test microorganisms Gram+ and Gram- bacteria and yeast. Out of 145 isolates, 51 showed antimicrobial activities against at least one test microorganism. 31 isolates inhibited only bacteria, 7 showed activity against bacteria and Candida albicans, and 13 displayed activity against C. albicans solely. Our findings suggest that Actinobacteria isolated from natural heavy metal ecosystems may be a valuable source of novel secondary metabolites and therefore of new biotechnologically promising antimicrobial compounds.

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection.

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

Food and lipid intake alters the pharmacokinetics of cyclosporine in kidney transplants.

Prevention of kidney graft rejection with cyclosporine leads to a large interindividual pharmacokinetic variability. However, food intake is likely to alter cyclosporine pharmacokinetics, and therefore its efficacy. The aim of our study was to evaluate the influence of food and lipid intake on cyclosporine pharmacokinetics. Twenty-four kidney grafted patients treated with Neoral® were included in this prospective monocentric study. In all patients, the pharmacokinetics of cyclosporine was evaluated in two occasions, after meal ('feed') and without meal ('fasting'). At each occasion, blood samples were collected at trough, and 0.5, 1, 2, 3, and 4 h after administration. Cyclosporine pharmacokinetics was described using a Bayesian pharmacokinetic model including two-compartments with first-order transfer and elimination rate constants, and a gamma absorption model. Influence of meal or olive oil, very common in Morocco, was tested as covariates on interoccasion variability parameters. Cyclosporine concentration-time data were satisfactorily described using the Bayesian pharmacokinetic model. Food intake significantly increased volume of distribution and decreased elimination of cyclosporine. The influence of oil intake explained a large part of this effect, suggesting that lipid intake was the main factor of pharmacokinetic variability due to food. This intake resulted in a decrease in area under the concentration curve between two administrations of 14.6%. Food, and especially lipid intake is likely to decrease the exposure to cyclosporine and may therefore lead to a decrease in treatment efficacy. Therefore, to ensure optimal immunosuppression in time, meal composition should remain as steady as possible.

Extracting backbones in weighted modular complex networks.

Network science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we propose and investigate two filter-based methods exploiting the overlapping community structure in order to extract the backbone in weighted networks. Indeed, highly connected nodes (hubs) and overlapping nodes are at the heart of the network. In the first method, called "overlapping nodes ego backbone", the backbone is formed simply from the set of overlapping nodes and their neighbors. In the second method, called "overlapping nodes and hubs backbone", the backbone is formed from the set of overlapping nodes and the hubs. For both methods, the links with the lowest weights are removed from the network as long as a backbone with a single connected component is preserved. Experiments have been performed on real-world weighted networks originating from various domains (social, co-appearance, collaboration, biological, and technological) and different sizes. Results show that both backbone extraction methods are quite similar. Furthermore, comparison with the most influential alternative filtering method demonstrates the greater ability of the proposed backbones extraction methods to uncover the most relevant parts of the network.

Isolation and screening of actinomycetes producing antimicrobial substances from an extreme Moroccan biotope.

INTRODUCTION: This study was carried out to isolate and screen actinomycetes from soil of two salterns in Taza-Morocco, for the production of antimicrobial compounds against a set of target bacteria. Also, it aims to highlight some practices in order to isolates actinomycetes and screen for their ability to produce antibacterial compounds. METHODS: Soil samples were analyzed for physical and chemical parameters including pH, electrical conductivity, and salinity. The actinomycetes were isolated on Casein Starch Agar (CSA) medium and purified on International Streptomyces Project 2 (ISP-2) medium. Antimicrobial activity of actinomycete isolates was evaluated by measuring the inhibition zone. These activities were tested against Dickeya solani IP2222, Pectobacterium brasiliensis 13471a, Escherichia coli K12, Proteus mirabilis, Pseudomonas aeruginosa CECT118, Listeria innocua CECT4030, Staphylococcus aureus CECT976, Bacillus subtilis DSM 347 and Candida alibicans, using three different culture media (CSA, Bennett and Mueller Hinton) and at two temperatures of incubation (30°C and 37°C). RESULTS: Physical and chemical analysis of soil samples showed that both sites are alkaline. Also, with regards to salinity, the second site showed to contain high salt concentration compared the first site. The abundance of bacteria isolated on CSA medium from both sites showed correlation with the physical-chemical properties of the sampling soils. Incubation temperature of 30°C resulted in a high number of actinomycetes (18/22) isolates with antimicrobial effect relative to the temperature of 37°C (4/22). Some actinomycetes isolates show antimicrobial effect on only one culture medium, which shows a special nutritional requirement to express their antimicrobial effect. On the other hand, some isolates, they express their antimicrobial effect on the three media at the same time. Additionally, some isolates of actinomycetes inhibit the growth of several microorganisms at once. While others inhibit the growth of only one microorganism tested which reflects a possible specificity of antimicrobial substances. CONCLUSION: Growth conditions including, media composition, temperature of incubation and the spectrum of test strain tailors the behavior of the antimicrobial screening.

Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone.

This paper deals with a new anisotropic discrete dual-tree wavelet transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional discrete dual-tree wavelet transform (DDTWT) by using the anisotropic basis functions associated with the hyperbolic wavelet transform instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The generalized Gaussian distribution is used to model the distribution of the sub-band coefficients. The estimated vector of parameters for each image is then used as input for the support vector machine classifier. Experiments were conducted on synthesized anisotropic fractional Brownian motion fields and on a real database composed of osteoporotic patients and control cases. Results show that the ADDTWT outperforms most of the competing anisotropic transforms with an area under curve rate of 93%.

[Pharmacogenetics and prediction of side effects of drugs]. / Pharmacogénétique et prédiction des effets indésirables des médicaments.

Drug response is often variable from one individual to another, which sometimes makes them difficult to use when the therapeutic range is narrow. This interindividual variability in response can be explained in part by genetic factors affecting the metabolism, transport and the mechanism of action of drugs. Pharmacogenetics studies the genetic mechanisms involved in the response to drugs in order to optimize drug therapy, both in terms of efficacy and job security. This article summarizes the most known present clinical applications that illustrate the benefit of pharmacogenetic tests available to the clinician and are feasible for routine therapeutic management of patients (prediction of efficacy and toxicity of drugs), but also to demonstrate the benefit of pharmacogenetic tests in terms of health economics (reducing the incidence of hospitalizations for adverse drug events).

A comparative study of the antioxidant scavenging activity of green tea, black tea and coffee extracts: a kinetic approach.

The antioxidant activities of three beverages, coffee, black tea and green tea, along with their major components, were investigated in terms of their reaction with the stable radical 2,2'-diphenyl-2-picrylhydrazyl (DPPH). We used a kinetic approach in parallel with quantification methods based on a fixed end-point to determine the scavenging efficiency of compounds abundant in these beverages during their reaction with DPPH using a stopped-flow spectrophotometer-based method. Ascorbic acid, (+)-catechin, (-)-epigallocatechin, tannic acid, and caffeic acid were selected as model antioxidants to study in coffee, black tea and green tea. We applied a second-order model to demonstrate similarities in the kinetics behavior of beverages and related compounds. Our findings showed the slopes k2(')((mol/L)(-1)s(-1)) and k2max(')((mol/L)(1)s(-1)) exhibited similar and correlated values; we suggest the variation in k2(') as a function of time is more informative about antioxidant properties than reaction with DPPH alone. We also used IC100 to test the reliability of the relative stoichiometry using a new comparative parameter "n", which was calculated as: n=c0DPPHIC100 (mol/L(mol/L)(-1), (mol/L)mlmg(-1) or molg(-1)).

HOS-based image sequence noise removal.

In this paper, a new spatiotemporal filtering scheme is described for noise reduction in video sequences. For this purpose, the scheme processes each group of three consecutive sequence frames in two steps: 1) estimate motion between frames and 2) use motion vectors to get the final denoised current frame. A family of adaptive spatiotemporal L-filters is applied. A recursive implementation of these filters is used and compared with its nonrecursive counterpart. The motion trajectories are obtained recursively by a region-recursive estimation method. Both motion parameters and filter weights are computed by minimizing the kurtosis of error instead of mean squared error. Using the kurtosis in the algorithms adaptation is appropriate in the presence of mixed and impulsive noises. The filter performance is evaluated by considering different types of video sequences. Simulations show marked improvement in visual quality and SNRI measures cost as well as compared to those reported in literature.

Methionine sulfoxide reductases protect Ffh from oxidative damages in Escherichia coli.

In proteins, methionine residues are primary targets for oxidation. Methionine oxidation is reversed by methionine sulfoxide reductases A and B, a class of highly conserved enzymes. Ffh protein, a component of the ubiquitous signal recognition particle, contains a methionine-rich domain, interacting with a small 4.5S RNA. In vitro analyses reported here show that: (i) oxidized Ffh is unable to bind 4.5S RNA, (ii) oxidized Ffh contains methionine sulfoxide residues, (iii) oxidized Ffh is a substrate for MsrA and MsrB enzymes; and (iv) MsrA/B repairing activities allow oxidized Ffh to recover 4.5S RNA-binding abilities. In vivo analyses reveal that: (i) Ffh synthesized in the msrA msrB mutant contains methionine sulfoxide residues and is unstable, (ii) msrA msrB mutant requires high levels of Ffh synthesis for growth and (iii) msrA msrB mutation leads to defects in Ffh-dependent targeting of MalF. We conclude that MsrA and MsrB are required to repair Ffh oxidized by reactive oxygen species produced by aerobic metabolism, establishing an as-yet undescribed link between protein targeting and oxidation.