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1. GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation

Author

Lin, Fengming, Xia, Yan, MacRaild, Michael, Deo, Yash, Dou, Haoran, Liu, Qiongyao, Cheng, Nina, Ravikumar, Nishant, and Frangi, Alejandro F.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the task becomes challenging. These shifts include differences in image appearance, intensity distribution, resolution, and aneurysm size, all of which complicate the segmentation process. To tackle these issues, we propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique coupled with boundary-aware contrastive learning (BACL). Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets. The results demonstrate that our proposed approach can extract more domain-invariant features, minimizing over-segmentation and capturing more complete aneurysm structures., Comment: Accepted by ISBI 2024

Published
2024

2. Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

Author

Lin, Fengming, Xia, Yan, MacRaild, Michael, Deo, Yash, Dou, Haoran, Liu, Qiongyao, Wu, Kun, Ravikumar, Nishant, and Frangi, Alejandro F.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and inter-domain shifts and most are unlabelled, UDA is more important while challenging in medical image analysis. This paper proposes a simple yet potent contrastive learning framework for UDA to narrow the inter-domain gap between labelled source and unlabelled target distribution. Our method is validated on cerebral vessel datasets. Experimental results show that our approach can learn latent features from labelled 3DRA modality data and improve vessel segmentation performance in unlabelled MRA modality data., Comment: Accepted by ISBI 2024

Published
2024

3. Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels

Author

Lin, Fengming, Xia, Yan, Ravikumar, Nishant, Liu, Qiongyao, MacRaild, Michael, and Frangi, Alejandro F

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment. However, existing methods face challenges in capturing small vessels and handling datasets that are partially or ambiguously annotated. In this paper, we propose an adaptive semi-supervised approach to address these challenges. Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training strategy, and boundary enhancement. Experimental results on 3DRA datasets demonstrate the superiority of our method in terms of mesh-based segmentation metrics. By leveraging the partially and ambiguously labeled data, which only annotates the main vessels, our method achieves impressive segmentation performance on mislabeled fine vessels, showcasing its potential for clinical applications., Comment: Accepted by DALI MICCAI 2023

Published
2023

4. Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels

Author

Lin, Fengming, Xia, Yan, Ravikumar, Nishant, Liu, Qiongyao, MacRaild, Michael, Frangi, Alejandro F., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xue, Yuan, editor, Chen, Chen, editor, Chen, Chao, editor, Zuo, Lianrui, editor, and Liu, Yihao, editor

Published
2024
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8. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

16. Brain Tumor Segmentation Using Dense Channels 2D U-net and Multiple Feature Extraction Network

Author

Shi, Wei, Pang, Enshuai, Wu, Qiang, Lin, Fengming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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17. Carbon-Dots-Mediated Improvement of Antimicrobial Activity of Natural Products

Author

Khan, Babar, Zhang, Jie, Durrani, Samran, Wang, Haiyan, Nawaz, Ali, Durrani, Faisal, Ye, Yonghao, Wu, Fu-Gen, and Lin, Fengming

Abstract

The development of new microbicidal compounds has become a top priority due to the emergence and spread of drug-resistant pathogenic microbes. In this study, blue-emitting and positively charged carbon dots (CDs), called Du-CDs, were fabricated for the first time utilizing the natural product extract of endophyte Diaporthe unshiuensisYSP3 as raw material through a one-step solvothermal method, which possessed varied functional groups including amino, carboxyl, hydroxyl, and sulfite groups. Interestingly, Du-CDs exhibited notably enhanced antimicrobial activities toward both bacteria and fungi as compared to the natural product extract of YSP3, with low minimum inhibitory concentrations. Moreover, Du-CDs significantly inhibited the formation of biofilms. Du-CDs bound with the microbial cell surface via electronic interaction or hydrophobic interaction entered the microbial cells and were distributed fully inside the cells. Du-CDs caused cell membrane damage and/or cell division cycle interruption, resulting in microbial cell death. Moreover, Du-CDs exhibited an improved antimicrobial effect and accelerated wound healing ability with good biocompatibility in the mouse model. Overall, we demonstrate that the formation of CDs from fungal natural products presents a promising and potential means to develop novel antimicrobial agents with great fluorescence, improved microbiocidal effect and wound healing capacity, and good biosafety for combating microbial infections.

Published
2024
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22. Hybrid Pyramid U-Net Model for Brain Tumor Segmentation

Author

Kong, Xiangmao, Sun, Guoxia, Wu, Qiang, Liu, Ju, Lin, Fengming, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Shi, Zhongzhi, editor, Mercier-Laurent, Eunika, editor, and Li, Jiuyong, editor

Published
2018
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25. Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study

Author

Liu, Qiongyao, primary, Sarrami-Foroushani, Ali, additional, Wang, Yongxing, additional, MacRaild, Michael, additional, Kelly, Christopher, additional, Lin, Fengming, additional, Xia, Yan, additional, Song, Shuang, additional, Ravikumar, Nishant, additional, Patankar, Tufail, additional, Taylor, Zeike A., additional, Lassila, Toni, additional, and Frangi, Alejandro F., additional

Published
2023
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33. Hemodynamics of thrombus formation in intracranial aneurysms: an in-silico observational study

Author

Liu, Qiongyao, primary, Sarrami-Foroushani, Ali, additional, Wang, Yongxing, additional, MacRaild, Michael, additional, Kelly, Christopher, additional, Lin, Fengming, additional, Xia, Yan, additional, Song, Shuang, additional, Ravikumar, Nishant, additional, Patankar, Tufail, additional, Taylor, Zeike A., additional, Lassila, Toni, additional, and Frangi, Alejandro F., additional

Published
2022
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40. Additional file 1 of Transmembrane transport process and endoplasmic reticulum function facilitate the role of gene cel1b in cellulase production of Trichoderma reesei

Author

Pang, Ai-Ping, Luo, Yongsheng, Hu, Xin, Zhang, Funing, Wang, Haiyan, Gao, Yichen, Durrani, Samran, Li, Chengcheng, Shi, Xiaotong, Wu, Fu-Gen, Li, Bing-Zhi, Lu, Zuhong, and Lin, Fengming

Abstract

Additional file 1:Figure S1. The mRNA level of cel1b in T. reesei RUT-C30 cultured in TMM + 2% cellulose for 120 h. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S2. (A) Plasmid construction for cel1b overexpression. Kan: kanamycin resistance; LB, left border; RB, right border; Ptcu1 or Pbxl1, tcu1 or bxl1 promoter; Ttrpc, Aspergillus nidulans trpC terminator; linker, a short sequence linked cel1b and DsRed; hyg, hygromycin B phosphotransferase gene. (B) PCR confirmation of cel1b overexpression in strains OEcel1B-Ptcu1 and OEcel1B-Pbxl1. T. reesei RUT-C30 was used as a control. M: DL5000 DNA marker, 1: OEcel1B-Ptcu1, 2: RUT-C30, 3: OEcel1B-Pbxl1, 4: RUT-C30. (C) Schematic map of replacing gene cel1b with marker hyg by homologous integration in strain KU70, generating cel1b deletion strain △cel1b. (D) PCR confirmation of the knockout of cel1b in strain △cel1b. T. reesei KU70 was used as the control. The absence of the gene cel1b was tested by primers designed to amplify the coding sequence of cel1b (Primer 1b shown in Figure S2C). The 5′ integration was tested with the forward primer targeting the genome region outside of the upstream homologous sequence and the reverse primer targeted hygromycin B encoding gene hph (Primer up shown in Figure S2C). For testing 3′ integration, the forward primer was located at the gene hph and the reverse primer was designed to targeting the genome region outside of the downstream homologous sequence (Primer do shown in Figure S2C). Figure S3. (Hemi)cellulase activities and protein secretion for T. reesei RUT-C30 and cel1b-overexpressing strains using three different promoters grown in TMM + 2% cellulose for 168 h. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S4. The mycelium length (A) and spore number (B) of T. reesei RUT-C30 and OEcel1B cultured in TMM + 2% cellulose for 168 h. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S5. Cellulase activities and protein secretion for T. reesei RUT-C30 and OEcel1B grown in TMM + 1% cellobiose or TMM + 2% lactose. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S6 qRT-PCR analysis of genes involved in cellulase production, including cellulase genes (A), transcriptional factors (B), and sugar transporters (C). Strains KU70 and △cel1b were cultured in TMM + 2% cellulose for 72 h. Figure S7. The colony diameter (A), sporulation ability (B), and morphology (C) of T. reesei KU70 and cel1b deletion strain △cel1b. All strains were cultured on TMM liquid or agar plates with 2% cellulose. The spores were counted at 120 h and cell morphology observation was performed at 72 h by CLSM. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Scale bar = 10 μm. Figure S8. Fluorescence intensity of the supernatants from OEcel1B and RUT-C30 grown in TMM + 2% cellulose for 168 h. Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S9 Confocal images of CEL1B-DsRed in strain OEcel1B cultured in TMM + 2% lactose. Scale bar = 10 μm. Figure S10. Confocal images of CEL1B-DsRed in strain OEcel1B cultured in TMM + 1% cellobiose. Scale bar = 10 μm. Figure S11. Sensitivity assay of the strains OEcel1B and RUT-C30 cultured in PDA agar plates with various concentration of Congo red (A) and NaCl (B). Data are represented as the mean of three independent experiments, and error bars express the standard deviations. Figure S12. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. The y axis represents the name of the most enriched pathways. GeneRatio: the number of DEGs in a specified GO term/ the number of the total DEGs in all GO terms; Count: the number of DEGs in a specified GO term; padj: p adjusted value.

Published
2022
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42. Additional file 1 of Sorbicillinoids hyperproduction without affecting the cellulosic enzyme production in Trichoderma reesei JNTR5

Author

Li, Chengcheng, Gu, Ruihan, Lin, Fengming, and Xiao, Huining

Abstract

Additional file 1: Fig. S1 The digital pictures of the T. reesei RUT-C30 and JNTR5 grown on PDA plates for 5 days. Fig. S2 The Copy numbers of gene Tr 69957 in strain JNTR5 (a) and the FPKM values of gene Tr 69957 in strain RUT-C30 and JNTR5 b. Fig. S3 Photographs taken under 302 nm UV light of the yellow pigments produced by strain JNTR5. The samples were obtained by cultivitating JNTR5 at 28 °C for 96 h with glucose as carbon source. Fig. S4 Confocal images of T. reesei RUT-C30 and JNTR5 observed by fluorescence microscopy by 10 X objective lens at Ex/Em = 488/498-563 nm. The samples were obtained by cultivitating RUT-C30 and JNTR5 at 28 °C for 48 h with glucose as carbon source. Fig. S5 The cellulase/hemicellulase activities of JNTR5. Error bars indicate SDs from three independently cultured replicates. Table S1 Primers used in this study

Published
2022
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49. Additional file 9 of Glutamine involvement in nitrogen regulation of cellulase production in fungi

Author

Pang, Ai-Ping, Zhang, Funing, Hu, Xin, Luo, Yongsheng, Wang, Haiyan, Durrani, Samran, Wu, Fu-Gen, Li, Bing-Zhi, Zhou, Zhihua, Lu, Zuhong, and Lin, Fengming

Abstract

Additional file 9: Figure S1. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. The y axis represents the name of the most enriched pathways. Figure S2. PCR confirmation of recombinant T. reesei strains △ooc1, △ooc2, and △ooc12.

Published
2021
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