Multi-objective Dwarf Mongoose Optimization Algorithm with Leader Guidance and Dominated Solution Evolution Mechanism

In the face of the increasingly complex multi-objective optimization problems, it is necessary to develop novel multi-objective optimization algorithms to meet the challenges. This paper proposes a multi-objective dwarf mongoose optimization algorithm (MODMO) with leader guidance and dominated solution dynamic reduction evolution mechanism. In the leader guidance mechanism, a dynamic trade-off factor is introduced to regulate the search radius of the scout mongoose exploring the mound. At the same time, an external archive is constructed with a non-inferior solution set and the leader is determined according to the non-dominated ranking level, and then the scout mongoose is guided to advance to the multi-objective frontier to improve the convergence of the algorithm. The dominant solution dynamic reduction evolution strategy is constructed to overcome the redundancy problem in the process of maintaining the external archive of non-inferior solutions. It dynamically selects the dominant solutions based on the dominance relationship and crowding distance and stores them in the external archive. The dominant solution information is integrated into the population evolution to realize the mining of multi-objective potential frontier and enhance the diversity of the algorithm. Compared with five representative algorithms on ZDT, DTLZ and WFG benchmark functions, experimental results show that MODMO algorithm has significant advantages in convergence and diversity

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

Comprehensive evaluation of extracellular small RNA isolation methods from serum in high throughput sequencing

Supplementary Tables S1-S9. (XLSX 576 kb

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation

Novel compounds in fruits of coriander (Coşkuner & Karababa) with anti-inflammatory activity

© 2020 Coriander, Coriandrum Sativum L., is one of the commonest food and medicinal plants in many countries, but its chemical ingredients and pivotal role in anti-inflammatory activity have not been fully explored. The present study aimed to identify new compounds in the fruits of coriander and explore their anti-inflammatory activity. The compounds were isolated by chromatographic seperations and identified using spectroscopic and spectrometric methods. RAW264.7 macrophage cells were used to detect the anti-inflammatory activity of the compounds via Griess assay, western blotting, ELISA, and flow cytometry methods. The study resulted in the discovery of four new compounds, which were identified as: 4α-(furo[2,3-d]pyrimidin-6′-ylmethyl)-9α-propylnonolactone (1), 4-(formyloxy)-4-(6′-methylcyclohex-1-en-1-yl)butanoate(2), (7α,8α)-3α-hydroxyl-12,13α-dimethyl-5(6)-en-bicyclo[5,3,0]caprolactone (3), 7-methoxy-4-methyl-5,6-dihydro-7H-(2-hydroxypropan-2-yl)furo[2,3-f] coumarin (4). Compound 3 showed the highest anti-inflammatory activity with IC50 of 6.25 μM for an inhibitory effect on nitrite oxide (NO) level. In addition, compound 3 decreased the lipopolysaccharides-stimulated generations of ROS and the inflammatory cytokines (IL-6 and TNF-α). Mechanism exploration indicated that compound 3 suppressed inflammatory mediators’ expression, like iNOS and COX-2. Furthermore, the NF-κB and MAPK pathways were involved in the anti-inflammatory process of compound 3

A Practical Quality Control Method for Saponins Without UV Absorption by UPLC-QDA

Saponins are a class of important active ingredients. Analysis of saponin-containing herbal medicines is a major challenge for the quality control of medicinal herbs in companies. Taking the medicine Astragali radix (AR) as an example, it has been shown that the existing evaporative light scattering detection (ELSD) methods of astragaloside IV (AG IV) has the disadvantages of time-consuming sample preparation and low sensitivity. The universality of ELSD results in an inapplicable fingerprint with huge signals from primary compounds and smaller signals from saponins. The purpose of this study was to provide a practical and comprehensive method for the quality control of the astragalosides in AR. A simple sample preparation method with sonication extraction and ammonia hydrolyzation was established, which shortens the preparation time from around 2 days to less than 2 h. A UPLC-QDA method with the SIM mode was established for the quantification of AG IV in AR. Methanol extract was subjected to UPLC-QDA for fingerprinting analysis, and the common peaks were assigned simultaneously with the QDA. The results showed that with the newly established method, the preparation time for a set of samples was less than 90 min. The fingerprints can simultaneously detect both saponins and flavonoids in AR. This simple, rapid, and comprehensive UPLC-QDA method is suitable for quality assessment of RA and its products in companies, and also provides references for the quality control of other saponin ingredients without UV absorption

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics

Assess and Summarize: Improve Outage Understanding with Large Language Models

Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are affected by a cloud outage, users can experience slow response times, connection issues or total service disruption, resulting in a significant negative business impact. Outages are usually comprised of several concurring events/source causes, and therefore understanding the context of outages is a very challenging yet crucial first step toward mitigating and resolving outages. In current practice, on-call engineers with in-depth domain knowledge, have to manually assess and summarize outages when they happen, which is time-consuming and labor-intensive. In this paper, we first present a large-scale empirical study investigating the way on-call engineers currently deal with cloud outages at Microsoft, and then present and empirically validate a novel approach (dubbed Oasis) to help the engineers in this task. Oasis is able to automatically assess the impact scope of outages as well as to produce human-readable summarization. Specifically, Oasis first assesses the impact scope of an outage by aggregating relevant incidents via multiple techniques. Then, it generates a human-readable summary by leveraging fine-tuned large language models like GPT-3.x. The impact assessment component of Oasis was introduced in Microsoft over three years ago, and it is now widely adopted, while the outage summarization component has been recently introduced, and in this article we present the results of an empirical evaluation we carried out on 18 real-world cloud systems as well as a human-based evaluation with outage owners. The results show that Oasis can effectively and efficiently summarize outages, and lead Microsoft to deploy its first prototype which is currently under experimental adoption by some of the incident teams

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation

Sequencing and Genetic Variation of Multidrug Resistance Plasmids in Klebsiella pneumoniae

BACKGROUND: The development of multidrug resistance is a major problem in the treatment of pathogenic microorganisms by distinct antimicrobial agents. Characterizing the genetic variation among plasmids from different bacterial species or strains is a key step towards understanding the mechanism of virulence and their evolution. RESULTS: We applied a deep sequencing approach to 206 clinical strains of Klebsiella pneumoniae collected from 2002 to 2008 to understand the genetic variation of multidrug resistance plasmids, and to reveal the dynamic change of drug resistance over time. First, we sequenced three plasmids (70 Kb, 94 Kb, and 147 Kb) from a clonal strain of K. pneumoniae using Sanger sequencing. Using the Illumina sequencing technology, we obtained more than 17 million of short reads from two pooled plasmid samples. We mapped these short reads to the three reference plasmid sequences, and identified a large number of single nucleotide polymorphisms (SNPs) in these pooled plasmids. Many of these SNPs are present in drug-resistance genes. We also found that a significant fraction of short reads could not be mapped to the reference sequences, indicating a high degree of genetic variation among the collection of K. pneumoniae isolates. Moreover, we identified that plasmid conjugative transfer genes and antibiotic resistance genes are more likely to suffer from positive selection, as indicated by the elevated rates of nonsynonymous substitution. CONCLUSION: These data represent the first large-scale study of genetic variation in multidrug resistance plasmids and provide insight into the mechanisms of plasmid diversification and the genetic basis of antibiotic resistance