Growth of Circulating Tumor Cell-Derived Colonies from Peripheral Blood of Melanoma Patients: Preliminary Characterization of Colony Composition

Circulating tumor cells (CTC) are rarely detected in the blood of cancer patients, even though they are a direct harbinger of eventual patient demise. We developed an innovative CTC culture technology to allow more sensitive isolation, expansion, and characterization of viable colonies from patient blood. In this assay, the entire leukocyte fraction from 10 ml of anticoagulated patient blood is placed into culture medium without any pre-selection. After 16 days in culture, CTC derived colonies are counted. As a proof-of-principle, blood samples from 58 Stage IIa-IV melanoma patients were tested. Ninety percent of these samples grew colonies. The colony numbers ranged from 0 - 308 (mean 63 ± 9.5 SEM). Ten normal volunteers had virtually no growth (mean 0.5 ± 1.4 colonies). Colonies were harvested using a micropipette for characterization. Tumor-cell containing spheroids were embedded in paraffin, sectioned, and stained with melanoma-specific mAb for histologic characterization. MITF proved to be the most consistent immunostain that identified melanoma cells in these colonies. A host-cell component in colonies was also identified using CD68 and CD43 mAb staining. Following enzymatic dissociation of colonies, a variety of immunostains were tested. Papanicolau staining proved most useful for identifying the abnormal nuclei of tumor cells. Flow cytometry could readily distinguish host and tumor cell populations based on DNA content and forward/side scatter in dissociated colonies. The stem cell marker ALDH1A1 associated with the aneuploid population, but CD45 was expressed on both diploid and aneuploid cells. The ability to repeatedly isolate CTC derived colonies from cancer patient blood samples opens the door to a novel type of long-term clinical monitoring. This novel CTC culture technology may prove useful to perform molecular characterization, assessment of treatment response, and testing of drug sensitivity and resistance in patients during treatment.

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

Improved artificial bee colony algorithm for pressure source parameter inversion of Sakurajima volcano from InSAR data

A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9,2020,to invert the magma source characteristics below the volcano based on the point source Mogi model.Considering that the Sakurajima volcano is surrounded by sea,all the deformation data are used to obtain the location and magma eruption volume of the volcano.In response to the weak local search capability of the artificial swarm algorithm,the difference between the global optimal individual and the un-roulette screened individual is introduced as the variance component in the onlooker stage.Detailed simulation experiments verify the improvement of the algorithm in terms of convergence speed.In real experiments,the Sakurajima volcano inversion shows closer fitting results and smaller residuals compared to the existing literature.Meanwhile,the convergence speed of the algorithm echoes with the simulation experiments.

Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Unrelated parallel machine scheduling problem(UPMSP)is a typical scheduling one and UPMSP with various reallife constraints such as additional resources has been widely studied;however,UPMSP with additional resources,maintenance,and energy-related objectives is seldom investigated.The Artificial Bee Colony(ABC)algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources,among other factors.In this study,an energy-efficient UPMSP with additional resources and maintenance is considered.A dynamical artificial bee colony(DABC)algorithm is presented to minimize makespan and total energy consumption simultaneously.Three heuristics are applied to produce the initial population.Employed bee swarm and onlooker bee swarm are constructed.Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met.Dynamical employed bee phase is implemented by computing resource shifting and solution migration.Computing resource shifting and feedback are used to construct dynamical onlooker bee phase.Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given.The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP.

Cobalt–Nickel Vanadate Nanonest Colonies Deposited Carbon Fabric as a Bifunctional Electrode for Li‑Ion Batteries and Oxygen‑Evolution Reactions

Transition metal vanadates(TMVs)have attracted significant attention in various research fields owing to their advantageous features.Furthermore,synthesizing TMVs directly on current collectors at the nanoscale is a promising strategy for achieving better performance.Herein,cobalt–nickel vanadate(CoV_(2)O_(6)–Ni_(2)V_(2)O_(7),CNV)was directly grown on carbon fabric using a facile one-step hydrothermal method.In particular,the CNV sample prepared for 3 h(CNV-3)exhibited a benefit-enriched nanonest-colony morphology in which abundant nanowires(diameter:10 nm)were intertwined,providing sufficient space for electrolyte diffusion.All the CNV electrodes exhibited good cycling performance in the lithium-ion battery study.Espe-cially,the CNV-3 electrode retained higher discharge and charge capacities of 616 and 610 mAh g-1,respectively at the 100th cycle than the other two electrodes owing to several morphologic features.The electrocatalytic activity of all the CNV samples for the oxygen-evolution reaction(OER)was also explored in an alkaline electrolyte.Among these CNV catalysts,the CNV-3 displayed excellent OER performance and required an overpotential of only 270 mV to drive a current density of 10 mA cm^(-2).The Tafel slope of this catalyst was also found to be low(129 mV dec^(-1)).Moreover,the catalyst exhibited excellent durability in a 24 h stability test.These results indicate that the metal vanadates with favorable nanostructures are highly suitable for both energy storage and water-splitting applications.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Tower crane path planning based on improved ant colony algorithm

In order to solve the problem of path planning of tower cranes,an improved ant colony algorithm was proposed.Firstly,the tower crane was simplified into a three-degree-of-freedom mechanical arm,and the D-H motion model was established to solve the forward and inverse kinematic equations.Secondly,the traditional ant colony algorithm was improved.The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function.Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability.In addition,the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points.Finally,according to the position of the hook,the kinematic inversion of the tower crane was carried out,and the variables of each joint were obtained.More specifically,compared with the traditional ant colony algorithm,the simulation results showed that improved ant colony algorithm converged faster,shortened the optimal path length,and optimized the path quality in the simple and complex environment.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

A Distributed Ant Colony Optimization Applied in Edge Detection

With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

History Teacher Training from Decolonial Curriculum Practices to Combat Racism at UniProjeção in the Federal District-A Secondary Publication

This article aims to understand the training process of history undergraduates,to see if there are decolonial curricular practices to combat racism at the Centro Universitário e Faculdade Projeção(UniProjeção)in the Federal District,to understand how coloniality has corroborated the exclusion of different epistemologies and the erasure of different cultures,and how this exclusionary process of coloniality interferes in the training of history teachers.In order to combat this practice,we are looking for alternatives that can break these suppressions carried out by Europeans.In this way,we turn to decolonial ideas that aim to break with the logic of coloniality.We can conclude that these practices are poorly developed in the institution,so we proposed active problem-based methodology and music as a didactic resource.As playful educational tools that strengthen the teaching-learning process,they are active agents in the decolonial work of combating racism,and it is essential to train responsible and ethical teachers in the fight against racism and any form of oppression.

Decolonizing African Agriculture

William Moseley o!ers a critical examination of why Westernled agricultural policies have often fallen short in Sub-Saharan Africa.Supported with compelling analysis,he argues that these failures stem from a colonial-based agricultural science,which prioritises power and political agendas over the unique needs of African communities.To e!ectively address food security,Moseley calls for a shift towards an indigenous agronomy that supports small-scale farmers through social innovation and local knowledge.

Application of Critical Discourse Analysis in Post-Colonial Literary Studies

Post-colonial literary studies, as a significant academic field, have recently garnered widespread attention from scholars due to their integration with critical discourse analysis. This study explores the application of critical discourse analysis in post-colonial literary research, focusing on how this method reveals implicit power relations, identity construction, and cultural conflicts within texts. Through in-depth analysis of representative works, this study demonstrates how critical discourse analysis provides new perspectives and methodological tools for post-colonial literary studies. The research finds that critical discourse analysis can effectively deconstruct colonial discourse in texts, reveal authors’ ideological positions, and offer profound insights into understanding the complexity of post-colonial societies. This study not only enriches the methodology of post-colonial literary research, but also provides valuable references for interdisciplinary studies.

k-center问题的算法研究综述

k-center问题是设施选址的基础问题,同样是NP难问题,在分配、紧急服务等领域也有着实际的应用。随着问题规模的扩大,原有的算法已不再适用,需要进一步优化或者改进。为了找到求解该问题的高效算法,对现有算法进行研究。对各类求解k-center问题的算法进行梳理,将求解算法划分为精确算法、启发式算法、元启发式算法、近似算法等,从算法原理、改进思路、性能和精度等方面进行对比综述。精确算法在求解小规模k-center问题时可在多项式时间内得到最优解,但是算法效率低,不适用于大规模问题;启发式算法可以在多项式时间内给出相对最优解,但是没有理论保证,无法衡量与最优解的关系;元启发式算法可根据对目前存在的智能优化算法进行改进,给出相对最优解,但是解的质量无法保证;利用近似算法得到的解具有近似比保证,有较大的理论研究价值,但是实用价值较弱。目前求解k-center问题的元启发式算法已取得一定的研究成果,但是在求解时间、求解规模、算法效率等方面仍待突破,这将是未来k-center问题的研究重点。

Phylogenetic Relationships and Status Quo of Colonies for Gayal Based on Analysis of Cytochrome b Gene Partial Sequences

Thirty-three mutations and four different haplotypes were found when cytochrome b（Cytb） gene partial sequences of 12 gayals were analyzed. Together with sequences of Bos indicus, Bos taurus, Bos grunniens, and Bos gaurus with Bubalus bubalis as the out group, the partial sequences of Cytb gene of gayals were aligned and base composition and nucleotide variation of Cytb gene were analyzed. The phylogenetic trees were constructed by the NJ method and the MP method respectively, both supporting almost the same topology. Gayal is an independent species of Bos from Bos indicus, Bos taurus, and Bos gaurus. The results also indicate that a great proportion of gayal bloodline was invaded by other species, and the protection of gayal is facing a formidable situation.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.