Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights into the Deep Phylogeny and Novel Divergence Time Estimation of Patellogastropoda from Complete Mitogenomes

To further understand the origin and evolution of Patellogastropoda, we determined the mitochondrial genome sequence of Cellana toreuma, and compared its mitogenome characteristics with the other four limpets of Nacellidae. The ratio of Ka and Ks indicated that these Nacellidae species were suffering a purifying selection, with exception of the atp6 gene. The gene sequence is basically consistent among families, while there are great differences among Lottidae species. According to the mitogenome sequences of selected gastropod species, we reconstructed a new phylogenetic tree with two methods. The data complement the mitogenome database of limpets and is a favorable research tool for the phylogenetic analysis of Gastropoda. It is found that there is a long-branch attraction (LBA) artefact in the family Lottiidae of Patellogastropoda. Therefore, the Patellogastropoda was separated by Heterobranchia, and Lottiidae is located at the root of the whole phylogenetic tree. Furthermore, we constructed the divergence time tree according to the Bayesian method and discussed the internal historical dynamics, and divergence differences among the main lineages of 12 Patellogastropoda under an uncorrelated relaxed molecular clock. In turn, we made a more comprehensive discussion on the divergence time of limpets at the molecular level.
Input-Aware Implication Selection Scheme Utilizing ATPG for Efficient Concurrent Error Detection

Recently, concurrent error detection enabled through invariant relationships between different wires in a circuit has been proposed. Because there are many such implications in a circuit, selection strategies have been developed to select the most valuable implications for inclusion in the checker hardware such that a sufficiently high probability of error detection ( P d e t e c t i o n ) is achieved. These algorithms, however, due to their heuristic nature cannot guarantee a lossless P d e t e c t i o n . In this paper, we develop a new input-aware implication selection algorithm with the help of ATPG which minimizes loss on P d e t e c t i o n . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated using error prone vectors. The evaluation results are then utilized to select the most efficient candidates for achieving optimal P d e t e c t i o n . The experimental results on 15 representative combinatorial benchmark circuits from the MCNC benchmarks suite show that the implications selected from our algorithm achieve better P d e t e c t i o n in comparison to the state of the art. The proposed method also offers better performance, up to 41.10%, in terms of the proposed impact-level metric, which is the ratio of achieved P d e t e c t i o n to the implication count.
Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.
α-Synuclein Conformational Plasticity: Physiologic States, Pathologic Strains, and Biotechnological Applications

α-Synuclein (αS) is remarkable for both its extensive conformational plasticity and pathologic prion-like properties. Physiologically, αS may populate disordered monomeric, helically folded tetrameric, or membrane-bound oligomeric states. Pathologically, αS may assemble into toxic oligomers and subsequently fibrils, the prion-like transmission of which is implicated in a class of neurodegenerative disorders collectively termed α-synucleinopathies. Notably, αS does not adopt a single “amyloid fold”, but rather exists as structurally distinct amyloid-like conformations referred to as “strains”. The inoculation of animal models with different strains induces distinct pathologies, and emerging evidence suggests that the propagation of disease-specific strains underlies the differential pathologies observed in patients with different α-synucleinopathies. The characterization of αS strains has provided insight into the structural basis for the overlapping, yet distinct, symptoms of Parkinson’s disease, multiple system atrophy, and dementia with Lewy bodies. In this review, we first explore the physiological and pathological differences between conformational states of αS. We then discuss recent studies on the influence of micro-environmental factors on αS species formation, propagation, and the resultant pathological characteristics. Lastly, we review how an understanding of αS conformational properties has been translated to emerging strain amplification technologies, which have provided further insight into the role of specific strains in distinct α-synucleinopathies, and show promise for the early diagnosis of disease.
Editorial: Mesenchymal Stromal Cells: Preclinical and Clinical Challenges

Department of Clinical Research, Emmaus Life Sciences, Torrance, CA, United States, University of Pisa, Pisa, Italy, Laboratory of Stem Cells and Regenerative Medicine, Department of Biomedical Sciences, CreatioProduction and Validation Center of Advanced Therapies, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain, Fujifilm Irvine Scientific, Inc, Santa Ana, CA, United States
Fast Iterative Adaptive Approach for Indoor Localization With Distributed 5G Small Cells

This letter addresses Time-Of-Arrival estimation for an indoor positioning system (IPS) made up of distributed 5G small cells by focusing on the Iterative Adaptive Approach (IAA) with Orthogonal Frequency Division Multiplexing (OFDM) signals. While IAA has been successfully applied to many other areas, its application to 5G IPS is not straightforward. In fact, even the current fast implementation of IAA is hardly compatible with 5G chips having limited computational capability, due to large number of sub-carriers of OFDM signals. To address this problem, we pursue the idea of averaging over a number of successive sub-carriers sequentially. Since our analysis shows that the direct Sub-carrier Average (SA) operation exhibits the asinc modulation effect on channel response, possibly leading to miss-detection of Direct Path (DP), we further propose a channel compensation-based SA method and provide a quantitative analysis of the signal-to-noise ratio of the DP to demonstrate its robustness. The resulting SA-based fast IAA (SA-FIAA) algorithm can achieve comparable performance as the standard IAA, while reducing computational cost by over 2 − 3 orders of magnitude, making it practically tractable in 5G IPSs. The effectiveness and computational efficiency of the proposed approach are well demonstrated through real-world experiments.
Expression of Clementine Asp-Rich Proteins (CcASP-RICH) in Tobacco Plants Interferes with the Mechanism of Pollen Tube Growth

Low-molecular-weight, aspartic-acid-rich proteins (ASP-RICH) have been assumed to be involved in the self-incompatibility process of clementine. The role of ASP-RICH is not known, but hypothetically they could sequester calcium ions (Ca2+) and affect Ca2+-dependent mechanisms. In this article, we analyzed the effects induced by clementine ASP-RICH proteins (CcASP-RICH) when expressed in the tobacco heterologous system, focusing on the male gametophyte. The aim was to gain insight into the mechanism of action of ASP-RICH in a well-known cellular system, i.e., the pollen tube. Pollen tubes of tobacco transgenic lines expressing CcASP-RICH were analyzed for Ca2+ distribution, ROS, proton gradient, as well as cytoskeleton and cell wall. CcASP-RICH modulated Ca2+ content and consequently affected cytoskeleton organization and the deposition of cell wall components. In turn, this affected the growth pattern of pollen tubes. Although the expression of CcASP-RICH did not exert a remarkable effect on the growth rate of pollen tubes, effects at the level of growth pattern suggest that the expression of ASP-RICH may exert a regulatory action on the mechanism of plant cell growth.
Flexible Design of the Connecting Plate in Pneumatic Soft Manipulators

This paper presents a novel connecting plate for achieving a flexible connection in a soft manipulator. The main material used in the connecting plate is silicone, which functions similarly to the bionic oblique muscle (BOM) of an octopus arm and takes over the two actuator segments. Each segment of the actuator consists of three chambers, and the chambers are assembled with the connecting plate via bolts and loops to be a manipulator. By using a flexible plate instead of a rigid one, the manipulator achieves more dexterous movements. This paper establishes a nonlinear model for analyzing the shear and compression deformation of a silicone connecting plate. Additionally, finite element method was used to analyze the mechanical performance of the connecting plate. The deformation of a soft material can reduce the stress concentration during movement of the manipulator. Therefore, in the experiment, the BOM exhibited better motion performance compared with the rigid connection plate.
Potential Efficacy of β-Amyrin Targeting Mycobacterial Universal Stress Protein by In Vitro and In Silico Approach

The emergence of drug resistance and the limited number of approved antitubercular drugs prompted identification and development of new antitubercular compounds to cure Tuberculosis (TB). In this work, an attempt was made to identify potential natural compounds that target mycobacterial proteins. Three plant extracts (A. aspera, C. gigantea and C. procera) were investigated. The ethyl acetate fraction of the aerial part of A. aspera and the flower ash of C. gigantea were found to be effective against M. tuberculosis H37Rv. Furthermore, the GC-MS analysis of the plant fractions confirmed the presence of active compounds in the extracts. The Mycobacterium target proteins, i.e., available PDB dataset proteins and proteins classified in virulence, detoxification, and adaptation, were investigated. A total of ten target proteins were shortlisted for further study, identified as follows: BpoC, RipA, MazF4, RipD, TB15.3, VapC15, VapC20, VapC21, TB31.7, and MazF9. Molecular docking studies showed that β-amyrin interacted with most of these proteins and its highest binding affinity was observed with Mycobacterium Rv1636 (TB15.3) protein. The stability of the protein-ligand complex was assessed by molecular dynamic simulation, which confirmed that β-amyrin most firmly interacted with Rv1636 protein. Rv1636 is a universal stress protein, which regulates Mycobacterium growth in different stress conditions and, thus, targeting Rv1636 makes M. tuberculosis vulnerable to host-derived stress conditions.
Effect of Blueberry Extract on Liver in Aged Rats

Aging and age-related disorders are prominent issues. Aging is associated with a gradual impairment of physiology at the genetic, cellular, tissue, and whole organism level that directly influences the development of chronic diseases and organ failure. Blueberries, on the other hand, are well known for their high content of bioactive compounds and have demonstrated positive impacts on metabolic factors that influence health and general well-being. This study is aimed at evaluating the ameliorating the effects of blueberry on the liver of aged rats by monitoring changes in metabolic disturbances, oxidative stress, and inflammatory disruption. The aged group of rats was orally administered with blueberry extract (200 mg/kg) for a period of 4 weeks. The results revealed that aging was associated with an increase in body weight, liver weight, and metabolic parameters like serum insulin, triglycerides, total cholesterol, and liver function markers accompanied with a decrease in vitamin D levels. Furthermore, the results showed a significant diminish in the activities of antioxidant enzymes, glutathione content with an elevation in lipid peroxidation, inflammatory mediators (tumor necrosis factor alpha, interleukin 6, and nuclear factor kappa-light-chain-enhancer of activated B cells) as well as fibrotic markers (TGF-β1) in the liver of aged rats. Compared to the young rats (control group), blueberry effectively reversed age-mediated disruption of the aforementioned parameters. Hence, blueberries can be used as a potential therapeutic strategy for the management of age-related liver dysfunction and disease.
Traffic Characterization Based Stochastic Modelling of Network-on-Chip

The trend towards multi-core and many-core processors has changed the landscape of computers and servers. Now the performance of a microprocessor heavily depends not only on the data path but also on the memory technology and communication technology. On-chip communication networks or Network-on-Chip is a component which facilitates communication between the cores of a microprocessor. In this manuscript we propose an analytical model for the analysis of Network-on-Chip performance based on Jackson queuing networks for applications that exhibit Poisson injection process. The injection process statistics and the traffic profile of PARSEC benchmarks have been characterized. We have shown that injection process of PARSEC benchmarks does not match with the well known probability distributions using Quantile-Quantile plot and Kolmogorov-Smirnov test. For realistic benchmarks that does not follow Poisson injection process we propose <inline-formula><tex-math notation="LaTeX">$G/D/1$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>G</mml:mi><mml:mo>/</mml:mo><mml:mi>D</mml:mi><mml:mo>/</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="adusumilli-ieq1-3191965.gif"/></alternatives></inline-formula> queuing model for the NoC router. We have carried out performance analysis using the traffic characterization in conjunction with our analytical model for prediction of performance metrics. The analytical model offers a speed up of around 13 times in comparison with the simulation based performance evaluation. The percentage of error between the analytical model and simulation is less than 6% for most of the benchmarks except Canneal and Ferret. We have also compared our analytical model results with the results of SNIPER simulator.
A comprehensive study of the radiative properties of NO—a first step toward a complete air opacity

In this paper we propose a methodology to calculate the radiative properties of the diatomic molecular constituents of air, and utilize the present approach to model the radiative properties of nitrogen monoxide, NO. We also investigate the important physics involved in calculating accurate radiative quantities for air, such as the Planck and Rosseland mean opacities, and emission and absorption coefficients, as well as the couplings accounted for in rovibrational calculations. Complete active space self-consistent field multi-reference configuration interaction (CAS-MRCI) calculations were performed in order to model the NO X 2Π, a 4Π, b 4Σ−, 12Σ+, 22Σ+, 32Σ+, G 2Σ−, B′2Δ, (C, B) 22Π, (H′, L) 32Π, and 12Φ adiabatic states, and calculate the respective molecular data. The γ X 2Π − A 2Σ+, ɛ X 2Π − D 2Σ+, β′ X 2Π − B′2Δ, ‘11 000 Å’ A 2Σ+ − D 2Σ+, ‘infrared’ X 2Π − X 2Π and X 2Π − (C, B) 22Π (δ and β) band systems are investigated in monochromatic spectra calculations, as well as the Ogawa a 4Π − b 4Σ− band and several other band systems. Several conclusions are drawn, such as the importance of including the Ogawa band, which has not been included in previous air radiative models or comprehensive line-list calculations, as well as the importance of performing coupled rovibrational line-list calculations in order to accurately calculate the Rosseland means. We also found that the additional band systems modeled here contribute significantly to the total Planck and Rosseland means.
Genetic impacts of physical disturbance processes in coastal marine ecosystems

Coastal habitats are among the most dynamic environments on earth and are highly vulnerable to large‐scale physical disturbance. Genetic studies of nearshore marine species are revealing long‐lasting signatures of major coastal disturbance events. We synthesize emerging data to highlight how physical perturbations can impact the phylogeographic patterns of coastal populations.
An ethnographic study of organizational performances in business services: Space, staging and materiality

It is said that all the world is a stage. But how do organizations physically stage performances such as sales pitches and research presentations? Drawing on a 14-month-long ethnographic study at a Fortune 500 strategic research company, this article explains how. Emphasizing the active role of human and non-human actors, it uncovers three staging practices that organizations use to transform spaces into stages. Organizations theme stages by populating them with certain objects. They produce a style of performance by arranging relationships between performers and audiences. Finally, they order movements from one stage to others so that plots emerge. Theorizing these staging practices through a materialist dramaturgy, the article challenges existing organizational theory that tends to focus on the ways organizations control and script performances. The article shows that organizational performances in service and knowledge organizations can be improvisational. They are not preordained but they are organized.
Music Data Feature Analysis and Extraction Algorithm Based on Music Melody Contour

Music is a way to reflect people’s real-life emotions, and listening to music has become an inseparable habit of the daily life. Text-based music information retrieval is still the main way for people to find music, but this method has obvious shortcomings and deficiencies, and it is a relatively cumbersome and inefficient method. In order to solve this problem, this paper proposes a feature extraction LAM algorithm based on the contour of music melody. Melody is the most important extraction feature in content-based music retrieval. Users can hum a song according to their own memory and then extract the rhythm, melody, and other information of the hummed audio information to match and identify the rhythm and melody features of the original song stored in the database. The retrieval method is based on the melody, rhythm, and other musical features of music and involves many issues such as the expression of musical melody, feature extraction of musical melody, user query construction, music melody matching, and music database construction. With the help of the customized query interface, media information can be retrieved. Finally, the experiment proves that the top-ten hit rate of the LAM algorithm after clustering is 91.3%, the top-three hit rate is 78.8%, and the first hit rate is 71.2%. The approximate symbol matching DP algorithm has a top-ten hit rate of 83.6%, a top-three hit rate of 66.4%, and a first hit rate of 63.6%. The method proposed in this paper has a high retrieval hit rate.
Synthesis, characterization of ‎2-amino-pyran ‎and derivatives evaluation of the bacterial, ‎antioxidant and anticancer activity

Using ethanol as a solvent, heterocyclic hexagonal rings for 2-amino-pyran derivatives (I33-I40) were produced by reacting a mole of chalcones derivatives with a mole of malononitrile. Physical properties such as melting point, colour, and molecular weight and spectroscopic measurements such as the infrared spectrum, [1H-NMR], [13C-NMR] spectrum were used to confirm the accuracy of the prepared compounds' compositions. The biological activity of various produced compounds was investigated in two species of pathogenic bacteria, one of which is Gram-positive, Staphylococcus aureus, and the other, Gram-negative, Escherichia coli. Chemical solutions for the two substances (I25, I32) with concentrations (0,01, 0.001, 0.0001) mg /ml were created using a solvent DMSO and Multer Hinton Agar growth medium. The antibiotic Ciprofloxacin was utilized as a control sample for the diffusion sensitivity test of the bacterial isolates used in the investigation. Using DPPH root and varied concentrations, the impact of chemical (I32) on the elimination of free radicals was also investigated. Breast cancer cells were taken from Baghdad's Medical City and used to test the efficiency of several produced chemicals against them.
Electron Spin Relaxation in Carbon Materials

This article focuses on EPR relaxation measurements in various carbon samples, e.g., natural carbons—anthracite, coal, higher anthraxolites, graphite; synthetically obtained carbons—glassy carbons, fullerenes, graphene, graphene oxide, reduced graphene oxide, graphite monocrystals, HOPG, nanoribbons, diamonds. The short introduction presents the basics of resonant electron spin relaxation techniques, briefly describing the obtained parameters. This review presents gathered results showing the processes leading to electron spin relaxation and typical ranges of electron spin relaxation rates for many different carbon types.