Improved cultural algorithms for job shop scheduling problem

This paper presents a new cultural algorithm for job shop scheduling problem. Unlike the canonical genetic algorithm, in which random elitist selection and mutational genetics is assumed. The proposed cultural algorithm extract the useful knowledge from the population space of genetic algorithm to form belief space, and utilize it to guide the genetic operator of selection and mutation. The different sizes of the benchmark data taken from literature are used to analyze the efficacy of this algorithm. Experimental results indicate that it outperforms current approaches using canonical genetic algorithms in computational time and quality of the solutions

Genetic changes in melanoma progression

Melanoma is a highly aggressive tumour with a poor prognosis for patients with advanced disease because it is resistant to current therapies. Therefore, the development of novel strategies for melanoma treatment is important. The characterization of the molecular mechanisms underlying melanoma proliferation, progression, and survival could help the development of novel targeted melanoma treatments. The MAPK and PI3K pathways both play important roles in melanoma progression. In the MAPK pathway, DUSP6, which acts as a phosphatase to negatively control the activation of ERK1/2, is involved in the development of human cancers. The MAPK pathway also regulates expression of the DNA repair gene ERCC1 following EGF treatment. ERCC1 is essential for nucleotide excision repair, which is one of the major systems for removal of cisplatin induced DNA lesions. The aims of this project were: 1, to investigate the molecular changes in our immortal mouse melanocyte cell lines that were needed for them to form tumours in a xenograft model; 2, to investigate whether the MAPK pathway regulates ERCC1 following cisplatin treatment and protects melanoma cells from death. Through comparison of the RAS/RAF/MEK/ERK (MAPK) and the PI3K/AKT (AKT) signalling pathways between our immortal mouse melanocyte cell lines and their tumour derivatives in our xenograft model, we identified a molecularly distinct subtype of mouse melanoma characterized by reduced ERK and AKT activity and increased expression of DUSP6. Functional analyses employing ectopic overexpression indicated that increased expression of DUSP6 enhanced anchorage independent growth ability and invasive ability in our mouse melanocytes, suggesting that increased DUSP6 expression may contribute to melanoma formation in the xenograft assay. We also demonstrated that higher expression of p-ERK suppressed invasion, but not anchorage independent growth, in our subtype of mouse melanoma by enforced expression of constitutively active MEK1 and MEK2. In addition, the role of DUSP6 in classical human melanoma was investigated in this Genetic changes in melanoma progression study. Inhibition of anchorage independent growth and invasion were observed after exogenous expression of DUSP6 in human melanoma cells. This suggested that DUSP6 played different roles in classic human melanoma than in our distinct subtype of mouse melanoma. Our study also investigated the phosphorylation level of ERK1/2 and the mRNA and protein level of ERCC1 and its partner XPF in the human melanoma cell line following cisplatin treatment. Significant increases in expression of p-ERK, ERCC1 and XPF were found in cisplatin treated cells. Moreover, a MEK inhibitor inhibited ERCC1 induction by cisplatin, but did not significantly affect XPF induction. This suggested that the MAPK pathway was involved in regulation of ERCC1 but not XPF. Furthermore, the DUSP6 level decreased after cisplatin treatment and overexpression of DUSP6 inhibited ERCC1 and XPF induction and reduced resistance to cisplatin. DUSP6 seems to play a crucial role in resistance of melanoma to cisplatin. In addition, a novel larger ERCC1 transcript was identified in human cell lines and was found to be upregulated by cisplatin. The ratio of larger ERCC1 transcript relative to the normal ERCC1 transcript increased following cisplatin treatment. The functions of this larger ERCC1 transcript in cisplatin resistance deserve further study

The Role of Long Noncoding RNAs in Gene Expression Regulation

Accumulating evidence highlights that noncoding RNAs, especially the long noncoding RNAs (lncRNAs), are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. The regulatory mechanisms of lncRNAs have been categorized into four major archetypes: signals, decoys, scaffolds, and guides. Increasing evidence points that lncRNAs are able to regulate almost every cellular process by their binding to proteins, mRNAs, miRNA, and/or DNAs. In this review, we present the recent research advances about the regulatory mechanisms of lncRNA in gene expression at various levels, including pretranscription, transcription regulation, and posttranscription regulation. We also introduce the interaction between lncRNA and DNA, RNA and protein, and the bioinformatics applications on lncRNA research

Correlation Analysis of Agricultural Injuries and Quality of Life Among Rural Residents in Hainan Province

Objective To analyze the correlation between agricultural injuries and quality of life among rural residents in Hainan Province, and to provide a scientific basis for agricultural injury prevention in Hainan. Methods Using a multi-stage random sampling method, one city (county) was randomly selected in each of the five directions of Hainan: east, south, west, north and central. 1-2 towns (townships) were selected in each of the selected cities (counties), then 5-10 natural villages were selected in each town, and 20-30 households were randomly selected in each village (neighbourhood committee) to conduct a face-to-face survey of all permanent residents aged 15 or above in the selected households. Results In both the no agricultural injury group and the group with agricultural injury, there were statistically significant differences (P<0.05) in the six dimensions of Physical Functioning, Role-Physical, Bodily Pain, General Health, Social Functioning and Role-Emotional and in the total score between the two groups, all with the no agricultural injury group scoring higher than the agricultural injury group. The incidence of agricultural injuries showed an overall decreasing trend as the quality of life score increased (P<0.05). Conclusion The incidence of agricultural injuries among rural residents in Hainan is related to the quality of life, and relevant measures should be taken to reduce the incidence of agricultural injuries and improve the quality of life of rural residents in Hainan

Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or low-data density, resulting in low model scalability or representation learning ability. For addressing this issue, a Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. It incorporates a proximal term into symmetry-aware and data density-oriented objective function for high representation accuracy. Then an adaptive Alternating Direction Method of Multipliers (ADMM)-based learning scheme is implemented through a Tree-structured of Parzen Estimators (TPE) method for high computational efficiency. Empirical studies on four UWNs demonstrate that PSNL achieves higher accuracy gain than state-of-the-art models, as well as highly competitive computational efficiency

A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning the limited interactions among numerous nodes. Knowledge acquisition from HDI data is a vital issue in the domain of data science due to their embedded rich patterns like node behaviors, where the fundamental task is to perform HDI data representation learning. Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence. However, existing LBI schemes are all statistic ones where the linear biases are fixed, which significantly restricts the scalability of the resultant NLFA model and results in loss of representation learning ability to HDI data. Motivated by the above discoveries, this paper innovatively presents the dynamic linear bias incorporation (DLBI) scheme. It firstly extends the linear bias vectors into matrices, and then builds a binary weight matrix to switch the active/inactive states of the linear biases. The weight matrix's each entry switches between the binary states dynamically corresponding to the linear bias value variation, thereby establishing the dynamic linear biases for an NLFA model. Empirical studies on three HDI datasets from real applications demonstrate that the proposed DLBI-based NLFA model obtains higher representation accuracy several than state-of-the-art models do, as well as highly-competitive computational efficiency.Comment: arXiv admin note: substantial text overlap with arXiv:2306.03911, arXiv:2302.12122, arXiv:2306.0364

Opening CALASYS to All Members

Since the Chinese American Librarians Association’s Academic Resources and Repository System (CALASYS, https://ir.cala-web.org/) was initiated in 2013, its collections have grown gradually by way of the Committee’s curation and entries with occasional help from LIS students. In order to resolve the bottleneck problems, promote CALASYS and expand its content, the 2020-2021 CALASYS Committee has strongly pursued the idea of opening CALASYS to all of the CALA members. The Committee began to implement the author self-contribution plug-in in the CALASYS’ Omeka platform in 2020. This poster will focus on the implementation of the self-contribution plug-in. It will cover the main steps and tasks of the implementation, including making metadata contribution templates, selecting copyright options, establishing contributor verification, testing workflow and developing end-user guide and back-end management documentations. It will also address the Committee’s work on creating training materials on workflow and metadata and plans on providing training sessions online to the CALA community. It will include the CALASYS’ history, its main features, collections, and usage statistics as well. By opening CALASYS to all members, it is hoped that it will better achieve the CALA’s strategic plan of 2020-2025, “Make CALA’s impact on local, state, national, and international levels.” Meanwhile, the bottleneck problems will be resolved and CALASYS will continue to grow at a faster pace in a more inclusive direction. The accompanying video is also available at: https://youtu.be/q9g4SXsnuO0