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"abstract": "Recommender system seeks to assist and augment the natural social process of making choices without sufficient personal experience of the alternatives. They have become fundamental applications in electronic commerce and information access, assisting users to effectively pinpoint information that of their interests from large catalog spaces. Contrary to the pervasive utilization of recommender systems in domains such as electronic commerce, the application of recommendation system in medical domain is limited and further effort is needed. In addition, while a variety of approaches have been proposed for performing recommendation, including collaborative filtering, demographic recommender and other techniques, each individual method has its own drawbacks. This paper proposes a medical oriented recommendation system in which patient’s background data is used to bootstrap the collaborative filtering engine and personalized suggestions are provided therein. We present empirical experiment results that show how the content-bootstrapped part of the system enhances the effectiveness of medical article recommendation of the collaborative filtering.",
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"abstract": "Many visual analytics tools exist to assist users in examining large amounts of information at once via coordinated views that include graphs, network connections and maps. However, the cognitive processes that those users undergo while using such tools remain a mystery. Many psychological studies suggest that individuals may undergo some planning stage followed by analysis before finally making conclusions when examining large amounts of analytical data with the goal of reaching a decision. While the general order of these cognitive states has been theorized, the exact states of individuals at specific points during their interaction with visual analytic systems remain unclear. In this work, we developed models to determine the cognitive states of users based solely on their interactions with visual analytics systems via Hidden Markov Models. Hidden Markov Models allow for the classification of observations through hidden states (cognitive states in our case) as well as the prediction of future cognitive states. We generate these models through unsupervised learning and use established metrics such as AIC and BIC metrics to evaluate our models. Our solutions are designed to help improve visual analytics tools by providing a better understanding of cognitive thought processes of users during data intensive analysis tasks.",
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"abstract": "Exploring digital libraries of scientific articles is an essential task for any research community. The typical approach is to query the articles’ data based on keywords and manually inspect the resulting list of documents to identify which papers are of interest. Besides being time-consuming, such a manual inspection is quite limited, as it can hardly provide an overview of articles with similar topics or subjects. Moreover, accomplishing queries based on content other than keywords is rarely doable, impairing finding documents with similar images. In this paper, we propose a visual analytic methodology for exploring and analyzing scientific document collections that consider the content of scientific documents, including images. The proposed approach relies on a combination of Content-Based Image Retrieval (CBIR) and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Additionally, we enable visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We show the effectiveness of our methodology through two case studies and a user evaluation, which attest to the usefulness of the proposed framework in exploring scientific document collections.",
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"content": "Exploring digital libraries of scientific articles is an essential task for any research community. The typical approach is to query the articles’ data based on keywords and manually inspect the resulting list of documents to identify which papers are of interest. Besides being time-consuming, such a manual inspection is quite limited, as it can hardly provide an overview of articles with similar topics or subjects. Moreover, accomplishing queries based on content other than keywords is rarely doable, impairing finding documents with similar images. In this paper, we propose a visual analytic methodology for exploring and analyzing scientific document collections that consider the content of scientific documents, including images. The proposed approach relies on a combination of Content-Based Image Retrieval (CBIR) and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Additionally, we enable visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We show the effectiveness of our methodology through two case studies and a user evaluation, which attest to the usefulness of the proposed framework in exploring scientific document collections.",
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"abstract": "Computer-aided image analysis of tissue sections is a modern accepted technique for tumor pathological diagnosis. With the advents of immunoscoring in tumor outcome evaluation, image-based immunoscore calculation has gained more and more attention. A new method of immunohistochemistry sections analysis, using the freeware ImageJ, is presented which can be applied to sections with either nuclear or cytoplasmic staining. The step-by-step process is presented and the method is tested using lung cancer tissues immunostained for cytokeratin 56, 18, NAPSIN, CGA, SYN, TTF, p63. This image analysis easily discriminates the positive staining and background, and quantified histogram profiles of the tested images, showing consistency with pathological diagnosis. The Spearman correlation between immunoscore and clinical TNM stage was 0.94. The method is easy to perform, without the need for previous image transformations. Further studies are needed to evaluate whether this method is efficient enough to be applied in clinical practice.",
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{
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"content": "Computer-aided image analysis of tissue sections is a modern accepted technique for tumor pathological diagnosis. With the advents of immunoscoring in tumor outcome evaluation, image-based immunoscore calculation has gained more and more attention. A new method of immunohistochemistry sections analysis, using the freeware ImageJ, is presented which can be applied to sections with either nuclear or cytoplasmic staining. The step-by-step process is presented and the method is tested using lung cancer tissues immunostained for cytokeratin 56, 18, NAPSIN, CGA, SYN, TTF, p63. This image analysis easily discriminates the positive staining and background, and quantified histogram profiles of the tested images, showing consistency with pathological diagnosis. The Spearman correlation between immunoscore and clinical TNM stage was 0.94. The method is easy to perform, without the need for previous image transformations. Further studies are needed to evaluate whether this method is efficient enough to be applied in clinical practice.",
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"title": "The cDNA Cloning and Expression of EGFP-PSF Fusion Protein in CHO Cell Line",
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"abstract": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
"abstracts": [
{
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"content": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
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"normalizedAbstract": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
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"id": "proceedings/itme/2015/8302/0",
"title": "2015 7th International Conference on Information Technology in Medicine and Education (ITME)",
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{
"id": "proceedings/icbeb/2012/4706/0/4706b750",
"title": "Study on the Effect of ER-alpha 36 siRNA on the Growth of ER Negative Breast Cancer Cells",
"doi": null,
"abstractUrl": "/proceedings-article/icbeb/2012/4706b750/12OmNzwpUbJ",
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"id": "proceedings/icbeb/2012/4706/0",
"title": "Biomedical Engineering and Biotechnology, International Conference on",
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{
"id": "trans/tb/2020/03/08476214",
"title": "In Silico Design and Experimental Validation of Combination Therapy for Pancreatic Cancer",
"doi": null,
"abstractUrl": "/journal/tb/2020/03/08476214/13WBGNazhGG",
"parentPublication": {
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"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
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{
"id": "trans/tb/2015/03/06965612",
"title": "Functional Impact of Autophagy-Related Genes on the Homeostasis and Dynamics of Pancreatic Cancer Cell Lines",
"doi": null,
"abstractUrl": "/journal/tb/2015/03/06965612/13rRUyeTVgt",
"parentPublication": {
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"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
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{
"id": "proceedings/bibm/2021/0126/0/09669151",
"title": "LncRNA PNKY is upregulated in breast cancer and promotes cell proliferation and EMT in breast cancer cells",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669151/1A9WhZQwxna",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"id": "trans/tb/2022/03/09257186",
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"doi": null,
"abstractUrl": "/journal/tb/2022/03/09257186/1oHhfyHmfjG",
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"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
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{
"id": "proceedings/bibm/2020/6215/0/09313457",
"title": "A T1D-associated lncRNA modulates the type I interferon signaling and the antiviral response in pancreatic beta cells",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313457/1qmfQqVvZhm",
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"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"id": "proceedings/bibm/2020/6215/0/09313502",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313502/1qmg4p6tp9m",
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"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
"displayVolume": "0",
"year": "2014",
"__typename": "ProceedingType"
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"article": {
"id": "12OmNxXCGHU",
"doi": "10.1109/BIBM.2014.6999329",
"title": "The clinical efficacy of transfusion of cytokine-induced killer cells combined with chemotherapy and XiaoJi decoction in patients with stage IIIB/IV non-small cell lung cancer",
"normalizedTitle": "The clinical efficacy of transfusion of cytokine-induced killer cells combined with chemotherapy and XiaoJi decoction in patients with stage IIIB/IV non-small cell lung cancer",
"abstract": "Objective: To evaluate the clinical efficacy and safety of cytokine-induced killer (CIK) cells immunotherapy in combination with platinum chemotherapy and Xiaoji decoction in patients with advanced stage non-small cell lung cancer (NSCLC). Methods: Patients with advanced stage NSCLC were randomly assigned to Group A (platinum chemotherapy plus xiaoji decoction) and Group B (platinum chemotherapy plus xiaoji decoction and CIK cell transfusion). Progression free survival (PFS), disease control rate (DCR), overall response rate (ORR), Karnofsky score (KPS), host cellular immune response and treatment related side effects were assessed. Results: Patients in Group B had significantly longer PFS than those in Group A. The overall response rate (ORR) and the disease control rate (DCR) showed no statistical difference between the Group B and Group A The level of CD3+, CD4+ were significantly higher after treatment. The level of CD3+ in Group A was significant lower after treatment. There were no immediate adverse reactions between the 2 groups. Conclusion: This study suggest that CIK cells immunotherapy combined with platinum chemotherapy and Xiaoji decoction may become an effective strategy to improve the survival and prolong the PFS of patients with advanced stage NSCLC.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Objective: To evaluate the clinical efficacy and safety of cytokine-induced killer (CIK) cells immunotherapy in combination with platinum chemotherapy and Xiaoji decoction in patients with advanced stage non-small cell lung cancer (NSCLC). Methods: Patients with advanced stage NSCLC were randomly assigned to Group A (platinum chemotherapy plus xiaoji decoction) and Group B (platinum chemotherapy plus xiaoji decoction and CIK cell transfusion). Progression free survival (PFS), disease control rate (DCR), overall response rate (ORR), Karnofsky score (KPS), host cellular immune response and treatment related side effects were assessed. Results: Patients in Group B had significantly longer PFS than those in Group A. The overall response rate (ORR) and the disease control rate (DCR) showed no statistical difference between the Group B and Group A The level of CD3+, CD4+ were significantly higher after treatment. The level of CD3+ in Group A was significant lower after treatment. There were no immediate adverse reactions between the 2 groups. Conclusion: This study suggest that CIK cells immunotherapy combined with platinum chemotherapy and Xiaoji decoction may become an effective strategy to improve the survival and prolong the PFS of patients with advanced stage NSCLC.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Objective: To evaluate the clinical efficacy and safety of cytokine-induced killer (CIK) cells immunotherapy in combination with platinum chemotherapy and Xiaoji decoction in patients with advanced stage non-small cell lung cancer (NSCLC). Methods: Patients with advanced stage NSCLC were randomly assigned to Group A (platinum chemotherapy plus xiaoji decoction) and Group B (platinum chemotherapy plus xiaoji decoction and CIK cell transfusion). Progression free survival (PFS), disease control rate (DCR), overall response rate (ORR), Karnofsky score (KPS), host cellular immune response and treatment related side effects were assessed. Results: Patients in Group B had significantly longer PFS than those in Group A. The overall response rate (ORR) and the disease control rate (DCR) showed no statistical difference between the Group B and Group A The level of CD3+, CD4+ were significantly higher after treatment. The level of CD3+ in Group A was significant lower after treatment. There were no immediate adverse reactions between the 2 groups. Conclusion: This study suggest that CIK cells immunotherapy combined with platinum chemotherapy and Xiaoji decoction may become an effective strategy to improve the survival and prolong the PFS of patients with advanced stage NSCLC.",
"fno": "06999329",
"keywords": [
"Immune System",
"Chemotherapy",
"Cancer",
"Platinum",
"Diseases",
"Lungs",
"Tumors",
"Effectiveness",
"Non Small Cell Lung Cancer",
"Cytokine Induced Killer Cells",
"Adjuvant Immunotherapy",
"Xiaoji Decoction"
],
"authors": [
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Liuning Li",
"givenName": "Liuning",
"surname": "Li",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangzhou University of Chinese Medicine, Guangzhou City, China",
"fullName": "Jiaying Liu",
"givenName": "Jiaying",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Xiaoshu Chai",
"givenName": "Xiaoshu",
"surname": "Chai",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Liwen Zhang",
"givenName": "Liwen",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Zhijian Chen",
"givenName": "Zhijian",
"surname": "Chen",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Chunxia He",
"givenName": "Chunxia",
"surname": "He",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangdong Provincial Hospital of Chinese Medicine",
"fullName": "Hongxi Hong",
"givenName": "Hongxi",
"surname": "Hong",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2014-11-01T00:00:00",
"pubType": "proceedings",
"pages": "74-75",
"year": "2014",
"issn": null,
"isbn": "978-1-4799-5669-2",
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"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2012/06470333/12OmNAtaRZQ",
"parentPublication": {
"id": "proceedings/bibmw/2012/2746/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
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{
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"doi": null,
"abstractUrl": "/proceedings-article/bmei/2008/3118b691/12OmNwDSdEI",
"parentPublication": {
"id": "proceedings/bmei/2008/3118/2",
"title": "BioMedical Engineering and Informatics, International Conference on",
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{
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"doi": null,
"abstractUrl": "/proceedings-article/isspit/2018/08642641/17QjJdGFWYr",
"parentPublication": {
"id": "proceedings/isspit/2018/7568/0",
"title": "2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)",
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},
{
"id": "proceedings/isspit/2018/7568/0/08642619",
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"doi": null,
"abstractUrl": "/proceedings-article/isspit/2018/08642619/17QjJdTaiHe",
"parentPublication": {
"id": "proceedings/isspit/2018/7568/0",
"title": "2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)",
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{
"id": "proceedings/bibm/2020/6215/0/09313551",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313551/1qmfUI3qFNu",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"proceeding": {
"id": "12OmNx4Q6EK",
"title": "2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)",
"acronym": "icmtma",
"groupId": "1002837",
"volume": "0",
"displayVolume": "0",
"year": "2015",
"__typename": "ProceedingType"
},
"article": {
"id": "12OmNyRg4s9",
"doi": "10.1109/ICMTMA.2015.102",
"title": "Down-Regulation of Survivin Expression by siRNA Suppresses Proliferation and Enhances Chemosensitivity in Human Pancreatic Cancer Cell Line Panc-1",
"normalizedTitle": "Down-Regulation of Survivin Expression by siRNA Suppresses Proliferation and Enhances Chemosensitivity in Human Pancreatic Cancer Cell Line Panc-1",
"abstract": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
"abstracts": [
{
"abstractType": "Regular",
"content": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "To explore effects of the siRNA of the survivin enhanced tumor sensitivity to chemotherapeutic drugs in human pancreatic cancer cells. A siRNA expression plasmid against the survivin gene were designed, transferred into human pancreatic carcinoma cell line Panc-1 and effected on the cell cycle. After 48 hours transfected, the expression of the survivin gene was inhibited significantly, and arrested the cell cycle in G1 phase, inhibited cell proliferation significantly, and promoted cell apoptosis. The growth of Panc-1 cells was decreased by 62.6% when the Panc-1 cell was transfected with siRNA after 48 hours, in comparison to the control group. Panc-1 cells presented an increase in apoptosis index and in cisplatin sensitivity. Our present results show that Small interfering RNA can exert a knockdown of the survivin gene expression in Panc-1 cells and effectively induce apoptosis and inhibit the growth and increase the chemotherapeutic drug sensitivity of human pancreatic cancer cell line Panc-1.",
"fno": "7143a400",
"keywords": [
"Cancer",
"Tumors",
"Sensitivity",
"Drugs",
"Immune System",
"RNA",
"Gene Therapy",
"Chemosensitivity",
"Panc 1",
"Survivin",
"RNA Interference",
"Gene Therapy"
],
"authors": [
{
"affiliation": null,
"fullName": "Kaiwen Guo",
"givenName": "Kaiwen",
"surname": "Guo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Wenjian Song",
"givenName": "Wenjian",
"surname": "Song",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yeli Gong",
"givenName": "Yeli",
"surname": "Gong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Song Hu",
"givenName": "Song",
"surname": "Hu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Wei Zhong",
"givenName": "Wei",
"surname": "Zhong",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Wenhong Qiu",
"givenName": "Wenhong",
"surname": "Qiu",
"__typename": "ArticleAuthorType"
}
],
"idPrefix": "icmtma",
"isOpenAccess": false,
"showRecommendedArticles": true,
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"pubDate": "2015-06-01T00:00:00",
"pubType": "proceedings",
"pages": "400-402",
"year": "2015",
"issn": null,
"isbn": "978-1-4673-7143-8",
"notes": null,
"notesType": null,
"__typename": "ArticleType"
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"webExtras": [],
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{
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{
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"title": "Biomedical Engineering and Biotechnology, International Conference on",
"__typename": "ParentPublication"
},
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{
"id": "proceedings/icbeb/2012/4706/0/4706b750",
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"doi": null,
"abstractUrl": "/proceedings-article/icbeb/2012/4706b750/12OmNzwpUbJ",
"parentPublication": {
"id": "proceedings/icbeb/2012/4706/0",
"title": "Biomedical Engineering and Biotechnology, International Conference on",
"__typename": "ParentPublication"
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"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2015/03/06965612",
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"doi": null,
"abstractUrl": "/journal/tb/2015/03/06965612/13rRUyeTVgt",
"parentPublication": {
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"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
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{
"id": "proceedings/bibm/2021/0126/0/09669151",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669151/1A9WhZQwxna",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"id": "proceedings/bibm/2020/6215/0/09313457",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313457/1qmfQqVvZhm",
"parentPublication": {
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"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
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{
"id": "proceedings/bibm/2020/6215/0/09313502",
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"abstractUrl": "/proceedings-article/bibm/2020/09313502/1qmg4p6tp9m",
"parentPublication": {
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"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"proceeding": {
"id": "1J6hnO7Gnao",
"title": "2022 IEEE 18th International Conference on e-Science (e-Science)",
"acronym": "e-science",
"groupId": "9973400",
"volume": "0",
"displayVolume": "0",
"year": "2022",
"__typename": "ProceedingType"
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"article": {
"id": "1J6hxoZwCVa",
"doi": "10.1109/eScience55777.2022.00022",
"title": "Multi-Tissue Analysis Using Synchrotron Radiation Micro-CT Images",
"normalizedTitle": "Multi-Tissue Analysis Using Synchrotron Radiation Micro-CT Images",
"abstract": "When studying metabolic disease, it is essential to investigate the disease's effect on multiple tissues and identify any communication, or cross-talk, between organs, tissues, and cells. In bone marrow cancer, adipose tissue triggers inflammation and growth of malignant plasma cells within the bone marrow and results in localized bone loss. Synchrotron radiation microtomography imaging enables 3D quantitative analysis of bone and adipose tissues and provides high resolution to observe local changes in tissue microstructure. However, optimal imaging techniques differ for hard bone tissues (absorption imaging) and soft adipose tissues (phase-contrast imaging). Here we introduce a new technique that leverages image reconstruction and deep learning in combination with the high-resolution imaging capabilities of synchrotron radiation microtomography to gain insight into the marrow microenvironment of human bone samples. This approach allowed for successful tissue segmentation and analysis of human core samples. Using high-resolution images such as these could allow for a better understanding of early bone-related changes that may predict disease progression or bone fractures.",
"abstracts": [
{
"abstractType": "Regular",
"content": "When studying metabolic disease, it is essential to investigate the disease's effect on multiple tissues and identify any communication, or cross-talk, between organs, tissues, and cells. In bone marrow cancer, adipose tissue triggers inflammation and growth of malignant plasma cells within the bone marrow and results in localized bone loss. Synchrotron radiation microtomography imaging enables 3D quantitative analysis of bone and adipose tissues and provides high resolution to observe local changes in tissue microstructure. However, optimal imaging techniques differ for hard bone tissues (absorption imaging) and soft adipose tissues (phase-contrast imaging). Here we introduce a new technique that leverages image reconstruction and deep learning in combination with the high-resolution imaging capabilities of synchrotron radiation microtomography to gain insight into the marrow microenvironment of human bone samples. This approach allowed for successful tissue segmentation and analysis of human core samples. Using high-resolution images such as these could allow for a better understanding of early bone-related changes that may predict disease progression or bone fractures.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "When studying metabolic disease, it is essential to investigate the disease's effect on multiple tissues and identify any communication, or cross-talk, between organs, tissues, and cells. In bone marrow cancer, adipose tissue triggers inflammation and growth of malignant plasma cells within the bone marrow and results in localized bone loss. Synchrotron radiation microtomography imaging enables 3D quantitative analysis of bone and adipose tissues and provides high resolution to observe local changes in tissue microstructure. However, optimal imaging techniques differ for hard bone tissues (absorption imaging) and soft adipose tissues (phase-contrast imaging). Here we introduce a new technique that leverages image reconstruction and deep learning in combination with the high-resolution imaging capabilities of synchrotron radiation microtomography to gain insight into the marrow microenvironment of human bone samples. This approach allowed for successful tissue segmentation and analysis of human core samples. Using high-resolution images such as these could allow for a better understanding of early bone-related changes that may predict disease progression or bone fractures.",
"fno": "612400a074",
"keywords": [
"Bone",
"Cancer",
"Computerised Tomography",
"Deep Learning Artificial Intelligence",
"Diseases",
"Image Reconstruction",
"Image Segmentation",
"Injuries",
"Medical Image Processing",
"Synchrotron Radiation",
"3 D Quantitative Analysis",
"Absorption Imaging",
"Bone Fractures",
"Bone Marrow Cancer",
"Deep Learning",
"Disease Progression",
"High Resolution Imaging Capabilities",
"Human Bone Marrow Microenvironment",
"Localized Bone Loss",
"Malignant Plasma Cells",
"Metabolic Disease",
"Multitissue Analysis",
"Phase Contrast Imaging",
"Soft Adipose Tissues",
"Synchrotron Radiation Micro CT Image Reconstruction",
"Synchrotron Radiation Microtomography Imaging",
"Tissue Microstructure",
"Tissue Segmentation",
"Deep Learning",
"Three Dimensional Displays",
"Statistical Analysis",
"Volume Measurement",
"Biological Tissues",
"Imaging",
"Bones",
"Bone",
"Bone Marrow",
"Bone Matrix",
"X Ray Imaging",
"Synchrotron Radiation Microtomography",
"Image Reconstruction",
"Phase Contrast",
"Phase Retrieval",
"Semantic Segmentation",
"Deep Learning",
"Machine Learning"
],
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{
"affiliation": "University of Utah,Department of Mechanical Engineering,Salt Lake City,UT,USA,84112",
"fullName": "Michael Sieverts",
"givenName": "Michael",
"surname": "Sieverts",
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{
"affiliation": "University of Utah,Department of Mechanical Engineering,Salt Lake City,UT,USA,84112",
"fullName": "Nikita Rabbitt",
"givenName": "Nikita",
"surname": "Rabbitt",
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{
"affiliation": "Advanced Light Source, Lawrence Berkeley Laboratory,Berkeley,CA,USA,94720",
"fullName": "Dilworth Y. Parkinson",
"givenName": "Dilworth Y.",
"surname": "Parkinson",
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{
"affiliation": "Huntsman Cancer Institute, University of Utah,Division of Hematology,Salt Lake City,UT,USA,84112",
"fullName": "Douglas W. Sborov",
"givenName": "Douglas W.",
"surname": "Sborov",
"__typename": "ArticleAuthorType"
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{
"affiliation": "University of Utah,Department of Mechanical Engineering,Salt Lake City,UT,USA,84112",
"fullName": "Claire Acevedo",
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"surname": "Acevedo",
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"proceeding": {
"id": "1dDLUMnsrPG",
"title": "2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)",
"acronym": "bigdataservice",
"groupId": "1808984",
"volume": "0",
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"year": "2019",
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"article": {
"id": "1dDLXXEFQFG",
"doi": "10.1109/BigDataService.2019.00053",
"title": "The Tumor Infiltrating Leukocyte Cell Composition Are Significant Markers for Prognostics of Radiotherapy of Rectal Cancer as Revealed by Cell Type Deconvolution",
"normalizedTitle": "The Tumor Infiltrating Leukocyte Cell Composition Are Significant Markers for Prognostics of Radiotherapy of Rectal Cancer as Revealed by Cell Type Deconvolution",
"abstract": "Tumor tissues consist of various types of cells including cancer stem cells, base cells, tumor infiltrated immune cells, and even new types of cancer associated cells. The tumor infiltrating cells play important roles in cancer progression and prognosis. Using present web servers and standalone tools of cell type deconvolution, including Timer, CIBERSORT, xCell, we deconvoluted the immune cell composition of microarray gene expression profiles of rectal cancer tissues collected from the rectal cancer patients before radiotherapy operation. By comparing the cell type composition obtained by timer of the rectal tissues from the radiotherapy responsive patients with those of non-responsive patients, we found that the content of CD4+ cells has average content of 0.1378 versus 0.1071 with p=0.0215; the ratio of CD4+/CD8+ is averaged 0.7869 versus 0.5564 with p=0.0210; and T cells CD4 memory resting and Eosinophils are both significantly higher with p-value of 0.033 and 0.0206, respectively, in radiotherapy responsive rectal cancer patient than in non-responsive cancer patients. The content of CD8+ cells are lower in the responsive than in non-responsive patients, which is averaged 0.1798 versus 0.2104 with p=0.0239. Other significant different cell types include macrophages M1 and M2, adipocytes, plasma cells and preadipocytes. Patients can be classified into responsive and non-responsive by machine learning method based on the tumor infiltrating immune cell composition with accuracy of 65%. It will be investigated that if the results can be applied to our newly collected data of other type of cancers including lung and nasopharyngeal carcinoma cancers in the future.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Tumor tissues consist of various types of cells including cancer stem cells, base cells, tumor infiltrated immune cells, and even new types of cancer associated cells. The tumor infiltrating cells play important roles in cancer progression and prognosis. Using present web servers and standalone tools of cell type deconvolution, including Timer, CIBERSORT, xCell, we deconvoluted the immune cell composition of microarray gene expression profiles of rectal cancer tissues collected from the rectal cancer patients before radiotherapy operation. By comparing the cell type composition obtained by timer of the rectal tissues from the radiotherapy responsive patients with those of non-responsive patients, we found that the content of CD4+ cells has average content of 0.1378 versus 0.1071 with p=0.0215; the ratio of CD4+/CD8+ is averaged 0.7869 versus 0.5564 with p=0.0210; and T cells CD4 memory resting and Eosinophils are both significantly higher with p-value of 0.033 and 0.0206, respectively, in radiotherapy responsive rectal cancer patient than in non-responsive cancer patients. The content of CD8+ cells are lower in the responsive than in non-responsive patients, which is averaged 0.1798 versus 0.2104 with p=0.0239. Other significant different cell types include macrophages M1 and M2, adipocytes, plasma cells and preadipocytes. Patients can be classified into responsive and non-responsive by machine learning method based on the tumor infiltrating immune cell composition with accuracy of 65%. It will be investigated that if the results can be applied to our newly collected data of other type of cancers including lung and nasopharyngeal carcinoma cancers in the future.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Tumor tissues consist of various types of cells including cancer stem cells, base cells, tumor infiltrated immune cells, and even new types of cancer associated cells. The tumor infiltrating cells play important roles in cancer progression and prognosis. Using present web servers and standalone tools of cell type deconvolution, including Timer, CIBERSORT, xCell, we deconvoluted the immune cell composition of microarray gene expression profiles of rectal cancer tissues collected from the rectal cancer patients before radiotherapy operation. By comparing the cell type composition obtained by timer of the rectal tissues from the radiotherapy responsive patients with those of non-responsive patients, we found that the content of CD4+ cells has average content of 0.1378 versus 0.1071 with p=0.0215; the ratio of CD4+/CD8+ is averaged 0.7869 versus 0.5564 with p=0.0210; and T cells CD4 memory resting and Eosinophils are both significantly higher with p-value of 0.033 and 0.0206, respectively, in radiotherapy responsive rectal cancer patient than in non-responsive cancer patients. The content of CD8+ cells are lower in the responsive than in non-responsive patients, which is averaged 0.1798 versus 0.2104 with p=0.0239. Other significant different cell types include macrophages M1 and M2, adipocytes, plasma cells and preadipocytes. Patients can be classified into responsive and non-responsive by machine learning method based on the tumor infiltrating immune cell composition with accuracy of 65%. It will be investigated that if the results can be applied to our newly collected data of other type of cancers including lung and nasopharyngeal carcinoma cancers in the future.",
"fno": "005900a301",
"keywords": [
"Cancer",
"Cellular Biophysics",
"Genetics",
"Learning Artificial Intelligence",
"Lung",
"Medical Diagnostic Computing",
"Patient Diagnosis",
"Radiation Therapy",
"Tumours",
"Web Services",
"Lung Cancer",
"CD 4 Memory Resting",
"T Cells",
"Eosinophils",
"Adipocytes",
"Microarray Gene Expression Profiles",
"Web Servers",
"Timer",
"CIBERSORT",
"X Cell",
"Cancer Prognosis",
"Machine Learning Method",
"Nasopharyngeal Carcinoma Cancers",
"Tumor Infiltrating Immune Cell Composition",
"Preadipocytes",
"Plasma Cells",
"CD 8 Cells",
"Nonresponsive Cancer Patients",
"Radiotherapy Responsive Rectal Cancer Patient",
"CD 4 Cells",
"Rectal Tissues",
"Cell Type Composition",
"Rectal Cancer Tissues",
"Cancer Progression",
"Cancer Associated Cells",
"Immune Cells",
"Cancer Stem Cells",
"Tumor Tissues",
"Cell Type Deconvolution",
"Tumor Infiltrating Leukocyte Cell Composition",
"Cancer",
"Gene Expression",
"Tumors",
"Deconvolution",
"Immune System",
"Matlab",
"Web Servers",
"Gene Expression Profiles",
"Radiotherapy Sensitivity Of Cancer Patients",
"Prognostic Markers",
"Cell Type Deconvolution"
],
"authors": [
{
"affiliation": "Chinese Academy of Sciences",
"fullName": "Min Zhu",
"givenName": "Min",
"surname": "Zhu",
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},
{
"affiliation": "Chinese Academy of Sciences",
"fullName": "Xiaolai Li",
"givenName": "Xiaolai",
"surname": "Li",
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},
{
"affiliation": "Chinese Academy of Sciences",
"fullName": "Yunjian Ge",
"givenName": "Yunjian",
"surname": "Ge",
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},
{
"affiliation": "Chinese Academy of Sciences",
"fullName": "Jinfu Nie",
"givenName": "Jinfu",
"surname": "Nie",
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},
{
"affiliation": "Chinese Academy of Sciences",
"fullName": "Xueling Li",
"givenName": "Xueling",
"surname": "Li",
"__typename": "ArticleAuthorType"
}
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"proceeding": {
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"title": "2017 IEEE International Conference on Big Data (Big Data)",
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"article": {
"id": "17D45WGGoLr",
"doi": "10.1109/BigData.2017.8258015",
"title": "Data context informed data wrangling",
"normalizedTitle": "Data context informed data wrangling",
"abstract": "The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.",
"fno": "08258015",
"keywords": [
"Automation",
"Manuals",
"Urban Areas",
"Big Data",
"Maintenance Engineering",
"Data Mining",
"Proposals",
"Data Wrangling",
"Data Context",
"Data Integration"
],
"authors": [
{
"affiliation": "School of Computer Science, University of Manchester, Manchester, UK",
"fullName": "Martin Koehler",
"givenName": "Martin",
"surname": "Koehler",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science, University of Manchester, Manchester, UK",
"fullName": "Alex Bogatu",
"givenName": "Alex",
"surname": "Bogatu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Informatics, University of Edinburgh, Edinburgh, UK",
"fullName": "Cristina Civili",
"givenName": "Cristina",
"surname": "Civili",
"__typename": "ArticleAuthorType"
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"abstract": "Complex data mapping tasks often arise in software engineering, particularly in code generation and model transformation. We describe Marama Torua, a tool supporting high-level specification and implementation of complex data mappings. Marama Torua is embedded in, and provides model transformation support for, our Eclipse-based Marama domain-specific language meta-tool. Developers can quickly develop stand alone data mappers and model translation and code import-export components for their tools. Complex data schema and mapping relationships are represented in multiple, high-level notational forms and users are provided semi-automated mapping assistance for large models. MaramaTorua is a set of Eclipse plug-ins allowing close integration with other tools such as schema browsers, and with the Marama meta-tool itself.",
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"abstract": "Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data. A widely used strategy for solving the inconsistencies is the multi-criterion combination. But one problem encountered in combining multiple criteria is the score normalization. In this paper, problems in existing methods are first analyzed, and a new gene importance transformation algorithm is then proposed. Experimental studies on three popular gene expression datasets show that the multi-criterion combination based on the proposed score correction and normalization produces gene rankings with improved robustness.",
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"content": "Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data. A widely used strategy for solving the inconsistencies is the multi-criterion combination. But one problem encountered in combining multiple criteria is the score normalization. In this paper, problems in existing methods are first analyzed, and a new gene importance transformation algorithm is then proposed. Experimental studies on three popular gene expression datasets show that the multi-criterion combination based on the proposed score correction and normalization produces gene rankings with improved robustness.",
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"title": "Modal Frequency Identification of Quayside Container Crane Based on Empirical Mode Decomposition and Power Spectrum",
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"abstract": "Hilbert-Huang Transform (HHT) causes modal aliasing and false modes in identifying modal frequencies of structures, thus a power spectrum modal frequency identification method based on Empirical Mode Decomposition (EMD) is proposed. The paper chooses quayside container crane (QCC) as the object of research. Firstly, the finite element model of QCC is established to identify the modal frequencies by modal analysis. Secondly, EMD is applied to the monitoring signals of QCC. According to the principle of correlation, the effective Intrinsic Mode Function (IMF) components are screened to identify the modal frequencies by power spectrum. The experimental results show that the method of frequency identification is close to the results of finite element frequency identification, and can effectively eliminate the false modes, improving the efficiency of frequency identification. The results of identification are more intuitive.",
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"content": "Hilbert-Huang Transform (HHT) causes modal aliasing and false modes in identifying modal frequencies of structures, thus a power spectrum modal frequency identification method based on Empirical Mode Decomposition (EMD) is proposed. The paper chooses quayside container crane (QCC) as the object of research. Firstly, the finite element model of QCC is established to identify the modal frequencies by modal analysis. Secondly, EMD is applied to the monitoring signals of QCC. According to the principle of correlation, the effective Intrinsic Mode Function (IMF) components are screened to identify the modal frequencies by power spectrum. The experimental results show that the method of frequency identification is close to the results of finite element frequency identification, and can effectively eliminate the false modes, improving the efficiency of frequency identification. The results of identification are more intuitive.",
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"authors": [
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"affiliation": "Tongji University",
"fullName": "Jiahui Liu",
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"surname": "Liu",
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"affiliation": "Tongji University",
"fullName": "Xianrong Qin",
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"affiliation": "Tongji University",
"fullName": "Qing Zhang",
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"affiliation": "Tongji University",
"fullName": "Xu Ding",
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{
"affiliation": "Tongji University",
"fullName": "Pengming Zhan",
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"content": "The problem of specifying a linear-shift-varying (LSV) filter to recover deterministic slowly-time-varying (STV) signals in the presence of noise or signal interference is reviewed. The LSV filter is specified based on the knowledge of the signal region of support in the time-frequency plane, or equivalently its instantaneous frequency (IF) and time-varying bandwidth (TVBW), which can be easily described using the short-time Fourier transform. A modified and more general LSV filter specification procedure based on the knowledge of the signal TVBW, IF, and rate of change of the IF is proposed. The filters produced by this procedure are shown to have a superior performance.<>",
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"abstract": "This paper presents a novel time-varying filtering (TVF) algorithm based on order time-varying short-time fractional Fourier transform (OTV-STFrFT) for multi-component signal analysis, which can process non-linear frequency modulated (NLFM). The idea of combining TVF and the order time-varying STFrFT are mainly inspired by the following two aspects: i) NLFM signals can be locally regarded as segmented linear frequency modulated (LFM) signals; ii) the fractional Fourier transform is the optimal sparse representation of LFM signal. The order time-varying STFrFT can overcome several defects of the existing TVF algorithms in dealing with multicomponent signals, of which the mixed components may intersect in the time-frequency distribution. The numerical results shows that the proposed algorithm is superior to the TVF algorithms based on conventional short-time Fourier transform (STFT) and state-of-the-art synchrosqueezed wavelet transforms (SsWT) in multi-component signal analysis.",
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"abstract": "Laser cutters are increasingly relevant within many user contexts and have become an essential tool for model building and prototyping. While providing precision and flexibility, these tools are typically suited for expert staff in industrial settings. VAL (Visually Augmented Laser cutting) proposes a novel system utilizing spatial augmented reality techniques to provide visual augmentation directly on the work surface. VAL involves projection of the user's model prior to and during laser cutting providing key benefits including minimizing idle time, reduction of errors, and support for new creative practices. We interview and observe laser cutter users to identify issues and concerns in the shared work context of a design school and describe the design process for our prototype, which aims to address these problems and unmet needs. Initial evaluation suggests VAL reduces complexity and raises user confidence. Our findings extend research on adapting new use contexts and creative practices with industrial fabrication tools.",
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"title": "1992 IEEE Multi-Chip Module Conference",
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"abstract": "The use of machine learning models to improve industrial production quality is becoming more popular year after year. The main reason is the huge data availability and the impressive boost of performance of such methods achieved in the last decade. In this work we propose an adaptation of three well known machine learning algorithms to estimate the quality of cut in industrial laser cutting machines. The challenge here is to use a pool of multimodal parameters coming from different sensors and fuse them in order to detect the cutting status of the machine in a near-online modality. We analyze then generative and discriminative approaches based on Gaussian Mixture Models, Recurrent Neural Networks, and Convolutional Neural Networks in a supervised setting. Results are computed on a brand-new dataset that is freely available for reference.",
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"content": "Laser assisted machining is an effective methods to improve the machinability of titanium alloy. Laser assisted micro-milling is often used in the cutting of difficult-to-machine materials. In this paper, variations of chip morphology and cutting force both in micro-milling and LAMM on Ti6Al4V were studied experimentally. It showed that, with the increase of feed per tooth, serration of chip becomes more and more prominent in both micro-milling and LAMM. Similar regularity can be found as cutting speed increases. When took the same feed per tooth or cutting speed, the cutting force amplitude measured in LAMM is much lower than that in micro-milling. And the sawtooth degree is also lower than that in micro-milling. The reason for these conclusions is that material soften by laser heating makes it easy to cut, which induced both non-prominent serration on chips and lower cutting forces needed.",
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