Refinement Method Research Articles

ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data.

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH Deorowicz et al. 2015 based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data.

Impact of Cobalt-Doping on the Optical, Magnetic, and Electronic Features of Hexagonal Cadmium Sulfide

CdS and Cd0.9Co0.1S samples were prepared under an N2 atmosphere. The structural analysis was conducted using X-ray diffraction. The structural and microstructure parameters were determined using Rietveld refinement method. The incorporation of cobalt ions into CdS matrix was confirmed by energy-dispersive spectroscopy and Fourier-transform infrared analysis. CdS sample has a non-magnetic feature while the Co-doped sample exhibited a magnetic behavior. The origin of magnetic property transformation has been investigated, revealing the emergence of ferromagnetic ordering and the conversion to a diluted magnetic semiconductor (DMS) with a calculated magnetic moment of 2.56 μ B upon Co doping. We also investigated how this Cobalt-doping-driven transformation affected optical, photoluminescence, and electronic properties. These effects correlated with the emergence of hyper-deep defect states. Electronic properties were calculated using density functional theory (DFT) with the HSE06 hybrid functional approximation. The calculated energy bandgaps for both Co-doped and pure CdS were 2.13 and 2.12 eV, respectively, while experimental measurements from our UV analysis yielded values of 2.26 and 2.15 eV. DFT calculations were employed to explore the magnetic properties, absorption coefficients, refractive indices, real and imaginary dielectric components, and energy loss spectra in both samples.

A Refinement Method for Single-Stage Object Detection Based on Progressive Decoupled Task Alignment

A Refinement Method for Single-Stage Object Detection Based on Progressive Decoupled Task Alignment

Humane Endpoints in Swiss Webster Mice Infected with Toxoplasma gondii RH Strain.

The highly virulent Toxoplasma gondii RH strain is maintained through successive passages in mice, but there is still a lack of studies that refine these procedures from a 3Rs perspective, where humanitarian ideals aim to minimize the stress, pain, or suffering of the animals used in the research without the loss of results. The aim of this study was to establish humane endpoints in Swiss Webster mice inoculated with the T. gondii RH strain. A total of 52 mice were infected with 5 × 106 tachyzoites/mL and monitored for periods of up to 5 days. The parameters body weight; hair condition; higher than normal body temperature; hypothermia; respiratory function; pain; soft stools or diarrhea; bloody diarrhea; tense, nervous, or in distress during handling; and ascites were recorded daily in score tables. The results showed that prominent piloerection, respiratory function, pain parameters, and ascites are important clinical signs to be used as a cut-off point for implementing euthanasia. The application of this refinement method helped to avoid animal suffering and pain without compromising the number of parasites recovered. We therefore suggest adopting these parameters in research protocols that require the maintenance of the T. gondii RH strain in murine models to avoid and reduce animal suffering.

A novel peridynamics refinement method with dual-horizon peridynamics

A novel peridynamics refinement method with dual-horizon peridynamics

Efficient synthesis and characterization of imidazo[1,2‐a]pyridine‐8‐carboxamide derivatives: A promising scaffold for drug development

AbstractImidazo[1,2‐a]pyridine, a pivotal fused heterocycle with widespread applications in medicinal chemistry, serves as a foundational scaffold for numerous pharmaceutical compounds. This present approach introduces a highly efficient multi‐step synthetic methodology for imidazo[1,2‐a]pyridine‐8‐carboxamide derivatives through a condensation reaction between 2‐aminonicotinic acid and chloroacetaldehyde in an environmentally friendly ethanol solvent followed by acid‐amine coupling reaction with a substituted amine. This coupling reaction employs an HATU catalyst and DIPEA strong base and affords excellent yields (93%–97%). A total of ten derivatives have been developed and elucidated by spectral methods, including FT‐IR, 1H NMR, and mass spectrometry. In summary, this synthetic strategy possesses favorable attributes for the synthesis of imidazo[1,2‐a]pyridine‐8‐carboxamide derivatives, such as good purity, readily available starting substrates, wide substrate applicability with simple scale‐up and convenient workup procedure. These characteristics render it a promising method for advanced refinement and prospective utilization in synthesizing crucial therapeutic agents.

Optimization of Extraction and Refining Parameters of Oil from Dotted Gizzard Shad (Konosirus punctatus).

To address the challenges associated with resource inefficiency, low extraction rates, environmental concerns, and high energy consumption in traditional fish oil production from dotted gizzard shad (Konosirus punctatus), a novel approach is needed. This study aimed to develop and evaluate two innovative methods for fish oil extraction and refinement, focusing on their effects on fish oil quality, fatty acid profile, and volatile compound composition throughout the respective processes. The findings of the study revealed that the ethanol-assisted enzymatic extraction method surpassed the conventional enzymatic approach in extraction efficiency, achieving an optimal extraction rate of 74.94% ± 0.45% under optimized process conditions. Moreover, the ethanol-NaOH one-step degumming and deacidification method proved effective in simultaneously removing phospholipids and free fatty acids. Under optimal conditions, a notable reduction in phospholipid content in dotted gizzard shad oil, from 6.80 ± 0.01 mg/g to 1.18 ± 0.01 mg/g, and a substantial decrease in acid value, from 3.31 mg/g to 0.31 mg/g, were observed. In summary, the study analyzed the physicochemical properties, fatty acid composition, and volatile components of fish oil before and after refinement. The refining process was found to preserve the fatty acid composition while efficiently eliminating hydroperoxides and reducing unpleasant odors in the crude oil.

Structural and dielectric features of (Bi0.5Na0.5)1−xBaxTiO3 lead-free ferroelectric ceramics: An approach to the phase diagram

(Bi0.5Na0.5)1−xBaxTiO3 lead-free ferroelectric ceramics were synthesized via the conventional solid-state reaction method. Structural and dielectric properties were investigated as a function of the doping concentration, considering x = 0, 2, 5, 8, 10, 12, 16, and 18 at. % Ba. The structural analyses were carried out from the x-ray diffraction technique, including the Rietveld refinement method, and Raman spectroscopy. Results confirmed the formation of the perovskite structure, revealing different crystalline symmetries, depending on the Ba2+ concentration: the single rhombohedral ferroelectric phase (R3c) for x = 0 and 2 at. %; coexistence of both rhombohedral ferroelectric (R3c) and tetragonal antiferroelectric (P4bm) phases for x = 5 at. % Ba; the single tetragonal antiferroelectric phase (P4bm) for x = 8 at. % Ba; coexistence of two tetragonal phases (antiferroelectric P4bm and ferroelectric P4mm) for x = 10 at. % Ba; and the single tetragonal ferroelectric phase (P4mm) for x = 12, 16, and 18 at. % Ba. The characteristics of the phases’ transition, investigated from dielectric analysis, revealed the presence of two dielectric anomalies, which indeed have been associated to different phases’ transitions, one of them showing relaxor-like characteristics. The obtained results offer new insights for a better understanding on the features of the phase diagram for the studied ceramic system, according to the different observed crystalline symmetries (ferroelectric and antiferroelectric) in a very wide doping concentration. In the light of the obtained results, a new phase diagram has been proposed considering a wider compositional range than those reported in the literature.

Role of powder morphology on α-phase content in plasma sprayed alumina coatings

Despite various studies addressing the retention of α phase in thermal sprayed alumina coatings, a noticeable research gap persists regarding the impact of feedstock morphology on phase retention and properties. In this work, we explored this simple way to enhance the retention of the α phase and studied its impact on mechanical and dielectric properties. To achieve this, we deposited alumina powders with two different morphologies (such as angular and spherical) with similar particle size using plasma spraying. After deposition, we quantified the retention of α phase present in the coatings using the Rietveld refinement method. From this analysis, we noticed that spherical morphology (SM) powder exhibited the highest percentage of α phase retention (82.81 %) as compared to angular morphology (AM) powder (48.8 %). In turn, SM-coating demonstrated superior mechanical properties, with improvements in hardness, elastic modulus and fracture toughness of 21.9 % 13.7 % and 25 % respectively, compared to AM-coating. This improved mechanical properties in SM-coating is attributed to high α phase retention, synergistic effect of both fully melted (FM) and partial melted (PM) regions of coating microstructure which hindered the crack propagation. Furthermore, the SM-coating exhibited superior dielectric properties, with a dielectric strength 9.9 % higher than the AM-coating. This dielectric performance is ascribed to high retained α phase and generation of discontinuous crack due to presence of PM regions in the SM-coating. These findings suggest the impact of feedstock powder morphology on coating performance and offer avenues for optimizing coating processes in diverse technological fields.

A protein network refinement method based on module discovery and biological information

BackgroundThe identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.MethodsBased on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.ResultsTo evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Strontium and Copper Co-Doped Multifunctional Calcium Phosphates: Biomimetic and Antibacterial Materials for Bone Implants.

β-tricalcium phosphate (β-TCP) is a promising material in regenerative traumatology for the creation of bone implants. Previously, it was established that doping the structure with certain cations can reduce the growth of bacterial activity. Recently, much attention has been paid to co-doped β-TCP, that is explained by their ability, on the one hand, to reduce cytotoxicity for cells of the human organism, on the other hand, to achieve a successful antibacterial effect. Sr, Cu-co-doped solid solutions of the composition Ca9.5-xSrxCu(PO4)7 was obtained by the method of solid-phase reactions. The Rietveld method of structural refinement revealed the presence of Sr2+ ions in four crystal sites: M1, M2, M3, and M4. The M5 site is completely occupied by Cu2+. Isomorphic substitution of Ca2+ → (Sr2+and Cu2+) expands the concentration limits of the existence of the solid solution with the β-TCP structure. No additional phases were formed up to x = 4.5 in Ca9.5-xSrxCu(PO4)7. Biocompatibility tests were performed on cell lines of human bone marrow mesenchymal stromal cells (hMSC), human fibroblasts (MRC-5) and osteoblasts (U-2OS). It was demonstrated that cytotoxicity exhibited a concentration dependence, along with an increase in osteogenesis and cell proliferation. Ca9.5-xSrxCu(PO4)7 powders showed significant inhibitory activity against pathogenic strains Escherichia coli and Staphylococcus aureus. Piezoelectric properties of Ca9.5-xSrxCu(PO4)7 were investigated. Possible ways to achieve high piezoelectric response are discussed. The combination of bioactive properties of Ca9.5-xSrxCu(PO4)7 renders them multifunctional materials suitable for bone substitutes.

Refinement and Computation Method for Line/Body Topological Relationships

Three-dimensional topological relationships serve as a theoretical foundation for quality control, update processing, and spatial analysis of three-dimensional spatial data in real-world three-dimensional GIS. The existing 3D topological relationship models are all basic relationship models that cannot distinguish the refined topological relationship between the line and the body with multiple intersections. In this study, we develop a 3D refined topological relationship description framework that draws from the two-dimensional refined topological relationship model, defines the unit intersection between the line and the body based on manifold topology, and proposes a method for describing the unit intersections between the line and the body considering Euler numbers and adjacency types. In total, 23 basic types between the line and the body are deduced. An example is provided to illustrate the distinguished refined topological relationship between the line and the body with multiple intersections. Subsequently, an algorithm for determining the basic type of line/body is developed. Finally, a line/body refined topological relationship computation prototype system is developed using the Nef polyhedron model, C++ language, and an open-source geometric algorithm library, and the effectiveness of our method is verified using actual building and pedestrian data.

Automatic multi-LiDAR calibration and refinement method

Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

Droplet Injection for Multiphase Rainfall Simulations on Dynamically Refined Mesh for Effective Interface Capturing

This paper presents a volume of fluid (VOF) model for multiphase rainfall simulation that injects rain droplets as a continuous phase alongside the adaptive mesh refinement (AMR) method to resolve air-water interfaces. An approach based on multi-level domains and a pseudo-random algorithm is proposed to determine injection locations in a random and non-intrusive manner with the least bias. Face zones instead of faces are adopted as constituent elements for droplet injection to prevent droplet fragmentation due to a change of face IDs caused by AMR. Correspondingly, a new multi-level address system is introduced to establish the correspondence among subdomains at different levels. The model is applied to multiphase flow VOF simulation for parametric 2-D and 3-D rainfall analysis. The results are verified by the data reported in the literature. It demonstrates that the model can efficiently simulate realistic rainfall phenomena, capture the droplet interface, and preserve droplet injection uniformity.

Aquavision Alchemy Illuminating Underwater Imagery Through Medium Transmission

Restoring underwater scenes often involves addressing interference caused by the underwater environment. Many existing methods overlook the scale-related characteristics inherent in these scenes. To tackle this, we propose a synergistic multi-scale detail refinement method for enhancing underwater scene details. This approach comprises multiple stages, starting with a low-degradation stage that enriches original images with multi-scale details using the Adaptive Selective Intrinsic Supervised Feature module. ASISF, through intrinsic supervision, precisely manages feature transmission across different degradation stages, refining multi-scale details while minimizing irrelevant information. Within the framework, we introduce the Bifocal Intrinsic-Context Attention Module, which efficiently utilizes multi-scale scene information by leveraging spatial contextual relationships. Throughout training, a multi-degradation loss function enhances the network's ability to extract information across various scales. The proposed method consistently outperforms state-of-the-art methods when evaluated against them. Key Words: Interference mitigation, Multi-scale detail refinement, Intrinsic supervision, Spatial contextual relationships.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs

BackgroundEnhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking.ResultsHere, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model.ConclusionsOur work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

A Survey of Dataset Refinement for Problems in Computer Vision Datasets

Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. 1 Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Adaptive mesh refinement in binary black holes simulations

We discuss refinement criteria for the Berger–Rigoutsos (block-based) refinement algorithm in our numerical relativity code GR-Athena++ in the context of binary black hole (BBH) merger simulations. We compare three different strategies: the ‘box-in-box’ approach, the ‘sphere-in-sphere’ approach and a local criterion for refinement based on the estimation of truncation error of the finite difference scheme. We extract and compare gravitational waveforms using the three different mesh refinement methods and compare their accuracy against a calibration waveform and demonstrate that the sphere-in-sphere approach provides the best strategy overall when considering computational cost and the waveform accuracy. Ultimately, we demonstrate the capability of each mesh refinement method in accurately simulating gravitational waves from BBH systems—a crucial aspect for their application in next-generation detectors. We quantify the mismatch achievable with the different strategies by extrapolating the gravitational wave mismatch to higher resolution.