Types Of Methods Research Articles

Control Charts for a Novel Exponent Power Rayleigh Distribution

Abstract: In this Paper, we presented the distribution as A Novel Exponent Power Rayleigh (NovEPRD) probability density function, Distribution, and Survival and Hazard functions introduced by Bharathi et al (2022) and A Novel Exponent Power Rayleigh (NovEPR) distribution Properties and its Applications studied by Bharathi et al (2022) and then here we derived the Weighted Variance (WV), Weighted Standard Deviation (WSD) and Correction Factor (CF) for existed A Novel Exponent Power Rayleigh (NovEPR) distribution , this paper obtains the parameters of the control lines X̅ and R drawn from the A Novel Exponent Power Rayleigh (NovEPR) distribution. These methods are compared with Newton Raphson’s simulations. For these control charts, Type I risk is compared with different groups of A Novel Exponent Power Rayleigh (NovEPR) distribution. Simulation results show that the Skewness Correction (SC) method of Type I carries less risk than other methods. When the distribution is approximately equal, the type I risk of the Shewhart, W V, WSD and SC X̅ plans is comparable, while the type I risk of the SC R plan is smaller

Development of a sequence-based in silico OspA typing method for Borrelia burgdorferi sensu lato.

Lyme disease (LD), caused by spirochete bacteria of the genus Borrelia burgdorferi sensu lato, remains the most common vector-borne disease in the northern hemisphere. Borrelia outer surface protein A (OspA) is an integral surface protein expressed during the tick cycle, and a validated vaccine target. There are at least 20 recognized Borrelia genospecies, that vary in OspA serotype. This study presents a new in silico sequence-based method for OspA typing using next-generation sequence data. Using a compiled database of over 400 Borrelia genomes encompassing the 4 most common disease-causing genospecies, we characterized OspA diversity in a manner that can accommodate existing and new OspA types and then defined boundaries for classification and assignment of OspA types based on the sequence similarity. To accommodate potential novel OspA types, we have developed a new nomenclature: OspA in silico type (IST). Beyond the ISTs that corresponded to existing OspA serotypes 1-8, we identified nine additional ISTs that cover new OspA variants in B. bavariensis (IST9-10), B. garinii (IST11-12), and other Borrelia genospecies (IST13-17). The IST typing scheme and associated OspA variants are available as part of the PubMLST Borrelia spp. database. Compared to traditional OspA serotyping methods, this new computational pipeline provides a more comprehensive and broadly applicable approach for characterization of OspA type and Borrelia genospecies to support vaccine development.

Inovasi Pendidikan Indonesia yang Efektif dan Efisien di Era Revolusi Industri 4.0

This research aims to find and understand the strategies needed in shaping / developing effective and efficient Indonesian Education Innovations in the industrial era 4.0. so that the Indonesian nation can prepare human resources who are ready to compete in the world, Indonesian society is prosperous and fully independent. This research uses a qualitative approach, with a case study type method, namely by obtaining data from data sources from scientific journals, scientific articles, e-books taken from the website: Google Scholar, Research Gate, OSC, and journals from certain universities. These data sources were taken by paying attention to the keywords of educational innovation, industrial revolution 4.0, efficient and effective educational innovation. After that, information that is revelant with this research is taken. The result of this research is that there are 5 ways that are strategic values to organize innovation: Basic education, namely family education and focusing on forming good character, nature and work ethic. Making innovations that focus on creating a supportive environment that gives freedom to what students are interested in. Focusing on STEM values in the academic community and based on Pancasila values. Focusing on strengthening the effectiveness of bureaucracy and policies produced by schools and universities. & Focusing on strengthening leadership values

A review of treatment of relapsing-remitting type of multiple sclerosis (RRMS)

Introduction Multiple sclerosis (SM) is the most common chronic inflammatory-demyelinating disease of the Central Nervous System. In its course, a multifocal damage is done to a nervous tissue - myelin sheaths surrounding axons become disintegrated. This process leads to a dysfunction of the electrical impulses passage along nerve fibers which occurs as various symptoms that hinder the functioning of the patients. Aim of the study This review aims to summarize treatment methods of relapsing-remitting type of multiple sclerosis (RRMS). Following information will be presented: groups of medications, mechanism of its action, its most important side effects and its effectiveness. Material and method This review presents the current state of knowledge about relapsing-remitting type of multiple sclerosis (RRMS) treatment - it is based on Pubmed database and was carried out using keywords concerning the issues of multiple sclerosis. Cited articles were selected in order to base on up-to-date investigation results. Summary A wide range of therapeutic options let neurologists treat their patients accurately, but the basic knowledge about each substance should be assimilated to choose the best individual therapy. Patients suffering from multiple sclerosis differ from each other and a personal approach is needed to implement appropriate treatment as soon as possible. Early start allows patients to not to lose their everyday functioning areas, as the neurodegenerative process is stopped at the initial stage. If some changes in brain tissue structure are permanently done, neurologists should know the options of symptomatic treatment to alleviate a patient's suffering.

Core genome multilocus sequence typing (cgMLST) applicable to the monophyletic Klebsiella oxytoca species complex.

The environmental bacterium Klebsiella oxytoca displays an alarming increase of antibiotic-resistant strains that frequently cause outbreaks in intensive care units. Due to its prevalence in the environment and opportunistic presence in humans, molecular surveillance (including resistance marker screening) and high-resolution cluster analysis are of high relevance. Furthermore, K. oxytoca previously described in studies is rather a species complex (KoSC) than a single species comprising at least six closely related species that are not easily differentiated by standard typing methods. To reach a discriminatory power high enough to identify and resolve clusters within these species, whole genome sequencing is necessary. The resolution is achievable with core genome multilocus sequence typing (cgMLST) extending typing of a few housekeeping genes to thousands of core genome genes. CgMLST is highly standardized and provides a nomenclature enabling cross laboratory reproducibility and data exchange for routine diagnostics. Here, we established a cgMLST scheme not only capable of resolving the KoSC species but also producing reliable and consistent results for published outbreaks. Our cgMLST scheme consists of 2,536 core genome and 2,693 accessory genome targets, with a percentage of good cgMLST targets of 98.31% in 880 KoSC genomes downloaded from the National Center for Biotechnology Information (NCBI). We also validated resistance markers against known resistance gene patterns and successfully linked genetic results to phenotypically confirmed toxic strains carrying the til gene cluster. In conclusion, our novel cgMLST enables highly reproducible typing of four different clinically relevant species of the KoSC and thus facilitates molecular surveillance and cluster investigations.

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses.

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann-Whitney U test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Pu-Thomas type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye

An artificial compound eye consists of multiple apertures that allow for a large field of view (FOV) while maintaining a small size. Each aperture captures a sub-image, and multiple sub-images are needed to reconstruct the full FOV. The reconstruction process is depth-related due to the parallax between adjacent apertures. This paper presents an all-in-focus 3D reconstruction method for a specific type of artificial compound eye called the electronic cluster eye (eCley). The proposed method uses edge matching to address the edge blur and large textureless areas existing in the sub-images. First, edges are extracted from each sub-image, and then a matching operator is applied to match the edges based on their shape context and intensity. This produces a sparse matching result that is then propagated to the whole image. Next, a depth consistency check and refinement method is performed to refine the depth of all sub-images. Finally, the sub-images and depth maps are merged to produce the final all-in-focus image and depth map. The experimental results and comparative analysis demonstrate the effectiveness of the proposed method.

Prior-guided adaptive generative adversarial network method for various types of borehole image inpainting

During geophysical exploration, inpainting defective logging images caused by mismatches between logging tools and borehole sizes can affect fracture and hole identification, petrographic analysis, and stratigraphic studies. However, existing methods do not describe sufficient stratigraphic continuity. In addition, they ignore the completeness of characterization in terms of fractures, gravel structures, and fine-grained textures in the logging images. To address these issues, we develop a deep-learning method for inpainting stratigraphic features. First, to enhance the continuity of image inpainting, we build a generative adversarial network and train it on numerous natural images to extract relevant features that guide the recovery of continuity characteristics. Second, to ensure that complete structural and textural features are found in geologic formations, we introduce a feature-extraction-fusion module with a cooccurrence mechanism consisting of channel attention (CA) and self-attention (SA). CA improves texture effects by adaptively adjusting control parameters based on highly correlated prior features from electrical logging images. SA captures long-range contextual associations across preinpainted gaps to improve completeness in fractures and gravel structure representation. Our method has been tested on various borehole images demonstrating its reliability and robustness.

Critical Analysis of Discipline Character Building through Discipline Week Activities for Students of Muhammadiyah 4 Banjarnegara Elementary School

This study aims to analyze the formation of the disciplinary character of grade 4 and 5 students at SD Muhammadiyah Banjarnegara. One way to shape the character of discipline is by carrying out Discipline Week activities, a collaboration between the school and Kodim 0704 Banjarnegara. With the implementation of the program, it is expected that the discipline of students will increase significantly. Discipline is an important attitude that affects the success of students both in the present and the future. This research uses the qualitative descriptive method of case study type. Data collection techniques are carried out by means of interviews, observation and documentation. In this case, researchers use data validity tests using triangulation techniques. The results showed that the activities of Discipline Week were carried out well and effectively. This activity can motivate students to be disciplined in time and behavior.

Image inpainting based on GAN-driven structure- and texture-aware learning with application to object removal

In this paper, a novel deep learning-based image inpainting framework consisting of restoring image structure and reconstructing image details from corrupted images is proposed. Most image inpainting methods in the literature aim at restoring image details, outlines, and colors, simultaneously, which may suffer from blurring, deformation, and unreasonable content recovery due to interference among various information. To solve these problems, a two-stage image inpainting deep neural network based on GAN (generative adversarial network) architecture is proposed. The proposed inpainting framework consists of two modules: (1) the first stage, called the structure-aware learning stage, aims at learning a GAN-based structure restoration network, focusing on recovering the low-frequency image component, including colors and outlines of the missing regions of the input corrupted image; and (2) the second stage, called the texture-aware learning stage, aims at learning a GAN-based detail refinement network, focusing on rebuilding the high-frequency image details and texture information. In particular, we also propose to remove details from the training images to better train the structure restoration network to avoid inadequate image structure recovery induced by richer image textures, where the detail reconstruction task is left to the second stage. This strategy achieves to balance the workload between the two stages and the image quality can be progressively enhanced through the two stages. Experimental results have shown that the proposed deep inpainting framework quantitatively and qualitatively achieves state-of-the-art performance on the well-known datasets, including the CelebA, Places2, and ImageNet datasets, compared with existing deep learning-based image inpainting approaches. More specifically, in terms of the two well-known image quality assessment metrics, PSNR (peak signal-to-noise ratio) and SSIM (structural similarity), the improvement percentage of the proposed method, compared with the baseline approach, respectively, ranges from 3.23 % to 11.12 %, and 1.95 % to 13.39 %. The improvements have been shown to stably and significantly outperform the compared state-of-the-art methods in most types of inpainting mask. We also show that the proposed method is applicable to image editing in object removal from a single image.

SaLTy: a novel Staphylococcus aureus Lineage Typer.

Staphylococcus aureus asymptomatically colonises 30 % of humans but can also cause a range of diseases, which can be fatal. In 2017 S. aureus was associated with 20 000 deaths in the USA alone. Dividing S. aureus isolates into smaller sub-groups can reveal the emergence of distinct sub-populations with varying potential to cause infections. Despite multiple molecular typing methods categorising such sub-groups, they do not take full advantage of S. aureus genome sequences when describing the fundamental population structure of the species. In this study, we developed Staphylococcus aureus Lineage Typing (SaLTy), which rapidly divides the species into 61 phylogenetically congruent lineages. Alleles of three core genes were identified that uniquely define the 61 lineages and were used for SaLTy typing. SaLTy was validated on 5000 genomes and 99.12 % (4956/5000) of isolates were assigned the correct lineage. We compared SaLTy lineages to previously calculated clonal complexes (CCs) from BIGSdb (n=21 173). SALTy improves on CCs by grouping isolates congruently with phylogenetic structure. SaLTy lineages were further used to describe the carriage of Staphylococcal chromosomal cassette containing mecA (SCCmec) which is carried by methicillin-resistant S. aureus (MRSA). Most lineages had isolates lacking SCCmec and the four largest lineages varied in SCCmec over time. Classifying isolates into SaLTy lineages, which were further SCCmec typed, allowed SaLTy to describe high-level MRSA epidemiology. We provide SaLTy as a simple typing method that defines phylogenetic lineages (https://github.com/LanLab/SaLTy). SaLTy is highly accurate and can quickly analyse large amounts of S. aureus genome data. SaLTy will aid the characterisation of S. aureus populations and ongoing surveillance of sub-groups that threaten human health.

Polymorphisms of HLA genes and hypersensitivity to penicillin among patients in a Taiwanese population.

Penicillin allergy is a potentially life-threatening condition that is common among patients. However, the genetic associations with penicillin allergy are not yet recognized for prevention or diagnosis, particularly in East Asian populations. We conducted a retrospective case-control study using data from the Taiwan Precision Medicine Initiative and analysing DNA samples to identify eight major MHC Class I and Class II loci. We employed imputation methods for accurate HLA typing and enrolled 17,827 individuals who received penicillin. Logistic regression analyses were utilized to explore associations between HLA genotypes, comorbidities and allergy risk, while simultaneously conducting a subgroup analysis to explore the association between HLA genotypes, comorbidities and the severity of allergic reactions. Our study assigned 496 cases to the penicillin allergy group and 4960 controls to a matched group. The risk of penicillin allergy was significantly higher with HLA-DPB1*05:01 (OR=1.36, p=.004) and HLA-DQB1*05:01 (OR=1.54, p=.03), with adjusted p-values of.032 and.24, respectively. Urticaria was identified as a separate risk factor (OR=1.73, p<.001). However, neither the HLA alleles nor the comorbidities had a significant relationship with the risk of severe penicillin-induced allergy. HLA-DPB1*05:01 was found to be significantly associated with penicillin allergy reactions among the Taiwanese population.

Crop type rather than production method determines functional trait composition of insect communities on arable land in boreal agricultural landscapes

Abstract To understand the potential consequences of arable land use changes for insect conservation and ecosystem functioning, it is fundamental to know how insect species with different functional traits respond to crop choice and production method. This study examined the effects of crop type and production method on functional traits of butterfly, bumblebee and carabid beetle communities using species abundance data from 78 fields in Southern Finland. Surrounding landscape composition was also accounted for. The studied traits were associated with dispersal capacity, habitat or diet specialization and phenology—the key determinants modifying species responses to agricultural disturbances and land use changes. Butterfly habitat breadth was narrowest and wingspan shortest in long‐term fallows. Fallows also supported the highest share of butterflies overwintering in early development stages and bumblebees with late‐emerging queens. The tongue length of bumblebees was longest in organic oat fields, probably due to flowering weeds with long corolla. For carabid beetles, the proportion of poor flyers and carnivores was highest in perennial crops and fallows. Carabid beetles overwintering as adults were relatively more abundant in organic than in conventional production, probably due to more intensive tillage in organic fields. In all insect groups, poor dispersers and/or specialists decreased with increasing arable land cover in the surrounding landscape. Increasing the area of long‐term fallows and perennial crops and enhancing within‐field plant diversity while maintaining landscape heterogeneity would promote insect species sensitive to agricultural disturbances and land use changes and their associated ecosystem services in boreal farmland.

Genomic characterization of Aeromonas spp. isolates from striped catfish with motile Aeromonas septicemia and human bloodstream infections in Vietnam.

Aeromonas spp. are commonly found in the aquatic environment and have been responsible for motile Aeromonas septicemia (MAS) in striped catfish, resulting in significant economic loss. These organisms also cause a range of opportunistic infections in humans with compromised immune systems. Here, we conducted a genomic investigation of 87 Aeromonas isolates derived from diseased catfish, healthy catfish and environmental water in catfish farms affected by MAS outbreaks in eight provinces in Mekong Delta (years: 2012-2022), together with 25 isolates from humans with bloodstream infections (years: 2010-2020). Genomics-based typing method precisely delineated Aeromonas species while traditional methods such as aerA PCR and MALDI-TOF were unable identify A. dhakensis. A. dhakensis was found to be more prevalent than A. hydrophila in both diseased catfish and human infections. A. dhakensis sequence type (ST) 656 followed by A. hydrophila ST251 were the predominant virulent species-lineages in diseased catfish (43.7 and 20.7 %, respectively), while diverse STs were found in humans with bloodstream infections. There was evidence of widespread transmission of ST656 and ST251 on striped catfish in the Mekong Delta region. ST656 and ST251 isolates carried a significantly higher number of acquired antimicrobial resistance (AMR) genes and virulence factors in comparison to other STs. They, however, exhibited several distinctions in key virulence factors (i.e. lack of type IV pili and enterotoxin ast in A. dhakensis), AMR genes (i.e. presence of imiH carbapenemase in A. dhakensis), and accessory gene content. To uncover potential conserved proteins of Aeromonas spp. for vaccine development, pangenome analysis has unveiled 2202 core genes between ST656 and ST251, of which 78 proteins were in either outer membrane or extracellular proteins. Our study represents one of the first genomic investigations of the species distribution, genetic landscape, and epidemiology of Aeromonas in diseased catfish and human infections in Vietnam. The emergence of antimicrobial resistant and virulent A. dhakensis strains underscores the needs of enhanced genomic surveillance and strengthening vaccine research and development in preventing Aeromonas diseases in catfish and humans, and the search for potential vaccine candidates could focus on Aeromonas core genes encoded for membrane and secreted proteins.

Molecular Diagnostics of Cryptococcus spp. and Immunomics of Cryptococcosis-Associated Immune Reconstitution Inflammatory Syndrome.

Cryptococcal infection poses a significant global public health challenge, particularly in regions near the equator. In this review, we offer a succinct exploration of the Cryptococcus spp. genome and various molecular typing methods to assess the burden and genetic diversity of cryptococcal pathogens in the environment and clinical isolates. We delve into a detailed discussion on the molecular pathogenesis and diagnosis of immune reconstitution inflammatory syndrome (IRIS) associated with cryptococcosis, with a specific emphasis on cryptococcal meningitis IRIS (CM-IRIS). Our examination includes the recent literature on CM-IRIS, covering host cellulomics, proteomics, transcriptomics, and genomics.

Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles.

In recent years, several deep learning-based methods have been proposed for predicting peptide fragment intensities. This study aims to provide a comprehensive assessment of six such methods, namely Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Prosit Transformer, and the method proposed by Guan et al. To this end, we evaluated the accuracy of the predicted intensity profiles for close to 1.7 million precursors (including both tryptic and HLA peptides) corresponding to more than 18 million experimental spectra procured from 40 independent submissions to the PRIDE repository that were acquired for different species using a variety of instruments and different dissociation types/energies. Specifically, for each method, distributions of similarity (measured by Pearson's correlation and normalized angle) between the predicted and the corresponding experimental b and y fragment intensities were generated. These distributions were used to ascertain the prediction accuracy and rank the prediction methods for particular types of experimental conditions. The effect of variables like precursor charge, length, and collision energy on the prediction accuracy was also investigated. In addition to prediction accuracy, the methods were evaluated in terms of prediction speed. The systematic assessment of these six methods may help in choosing the right method for MS/MS spectra prediction for particular needs.

New Vaccine Therapy for Triple-Negative Breast Cancer

Abstract Purpose of the review The objective of this review is to provide an analysis of early-phase clinical trials investigating vaccine therapies for triple-negative breast cancer (TNBC). Specifically, the focus is on ongoing trials that are actively recruiting or in progress, while excluding vaccines that target neoantigens or those that have already completed trials. Recent findings Over the past decade, notable transformations have occurred in the strategy of breast cancer vaccine design. Traditional approaches to identifying tumor antigens, such as SEREX, have been replaced with modern techniques, such as RNA sequencing, HLA typing, and immunoinformatics. These new methods enable the identification and characterization of tumor antigens. Notably, current clinical investigations into tumor targets extend beyond mutated self-proteins or proteins that are overexpressed following neoplastic transformation. Clinical researchers are currently examining protein targets associated with cancer stem cells or non-malignant immune regulatory cell types within the tumor microenvironment. However, the application of up-to-date antigen delivery methods for certain types of breast cancer vaccine therapies still lags behind. Another significant transformation in comparison to previous breast cancer vaccine therapies is the emphasis on stimulating robust T-cell responses against breast cancer cells, independent of any B-cell response directed at the tumor. Summary In conclusion, we critically assessed the tumor antigens targeted by vaccine immunotherapies in these new clinical trials, the delivery methods used for these antigens, and conclude by discussing potential future directions for the development of new TNBC vaccine therapies.

A comparison between physics-based Li-ion battery models

Physics-based electrochemical battery models, such as the Doyle-Fuller-Newman (DFN) model, are valuable tools for simulating Li-ion battery behavior and understanding internal battery processes. However, the complexity and computational demands of such models limit their applicability for battery management systems and long-term aging simulations. Reduced-order models (ROMs), such as the Extended Single Particle Model (ESPM), Single Particle Model (SPM) and Polynomial and Padé approximations, here all referred to as simplifications, lead to faster computational speeds. Choosing the appropriate simplification method for a specific cell type and operating condition is a challenge. This study investigates the simulation accuracy and calculation speed of various simplifications for high-energy (HE) and high-power (HP) batteries at different current loading conditions and compares those to the full-order DFN model. The results indicate that among the ROMs, the ESPM consistently offers the best combination of high computational speed and relatively good accuracy in most conditions in comparison to the full-order DFN model. Among the approximations, higher-order polynomial approximation, third and fourth-order Padé approximation perform the best in terms of accuracy. The higher-order polynomial approximation shows an advantage in terms of computing speed, while the fourth-order Padé approximation achieves the highest overall accuracy among the different approximations.

Deformation mechanism and minimum energy path in Silicon–graphite composites with lattice defects

Edge-on atomic layers in layered solids undergo buckling, which creates a structure referred to as a “ripplocation.” A collective set of multiple ripplocations is referred to as a “ripplocation boundary.” In this study, in order to investigate the impact of lattice defects on ripplocation, we investigate the buckling deformation of graphene with lattice defects under confinement. To this end, we conducted confined uniaxial compression simulations by placing graphene sheets with various lattice defects between silicon atomic layers. Different types of lattice defects affect the mechanical properties of graphene, the results show that Young's modulus of graphene with the (1,0)_5 dislocation pair was the maximum at 494.6 GPa, whereas the Young's modulus of the (1,2)_10 dislocation pair was the minimum at 309.5 GPa. In addition, we employed a differential geometry method to study the out-of-plane deformation of a single-layer graphene system. Further, we used the nudged elastic band method for various types of lattice defects in graphene to uncover the minimum transition pathways, activation energy required to move from the (1, 0) dislocation pair to the (1, 1) pair is 44.916 eV. In particular, the results indicate that graphene with lattice defects exhibit kink deformation after buckling compared to that of perfect graphene. Our study not only explores the deformation of ripplocations and kink boundaries in layered solids but also provides a more comprehensive description influencing lattice defects on the nucleation mechanism and mechanical changes of ripplocations in graphene.

Numerical methods for highly oscillatory Volterra integral equations with general oscillators

In this paper, collocation methods for linear Volterra integral equations with highly oscillatory trigonometric kernels are considered. Different from the cases with linear oscillator, we need a further action to obtain a practical scheme for general oscillators. To deal with it, the moment free Filon type method is employed in the exact collocation equation resulting to a fully discretized one. In order to conduct the convergence analysis, we firstly study the highly oscillatory integrals occurring in the error equations. Both the theoretical and numerical results show that our method not only converges with respect to step length but also has an asymptotic order which could be two. The asymptotic order indicates that the numerical results will become more accurate as the increase of frequency.