Anwendung Massenspektrometrie basierter Technologie zur Entdeckung räumlicher Peptidsignaturen in der Krebsforschung

Cancer is one of the leading causes of death worldwide, within the molecular and structure complexity of tumors are causal factors for disease progression and treatment standards. With the development of molecular biological techniques, physicians could use genetic variation or protein and metabolic expression profile besides histo-morphologicial evaluation to classify more accurate risk assessment and to guide treatment decisions. The biomarker-driven personalized therapies might improve clinical care, avoid unnecessary treatments and reduce the duration and costs for hospital stay. Therefore, there is a strong demand for more reliable molecular biomarker profiles. In this dissertation, a novel technique called imaging mass spectrometry (MADLI-MSI) is used to investigate the potential of spatially resolved peptide signatures (directly from tumor tissue; in situ) for (i) discrimination of subtypes of serous ovarian cancer (HGSOC) and (ii) risk assessment of neuroblastoma. Univariate and multivariate static methods were used to determine associated peptide signatures. Using complementary methods, liquid chromatography-based mass spectrometry the corresponding proteins to the peptides were identified and verified by immunohistology. Consequently, peptide signatures were identified to predict disease recurrence in early-stage HGSOC patients and to distinguish high-risk neuroblastoma patients from other risk groups. These results suggest that the MALDI-MSI technique is a promising analytical method that facilitates diagnosis and treatment decision-making. It has also provided new biological insights into tumor heterogeneity, that could benefit the development of molecular biomarker profiles. The data of this dissertation have been really published in Journal “Cancers (MDPI)” 2020 and 2021.Onkologische Erkrankungen (Krebs) sind weltweit eine der häufigsten Todesursachen. Die molekulare und strukturelle Komplexität von Tumoren sind ursächlich für die Krankheitsprogression und Therapieanspruch. Mit der Entwicklung von neuen molekularbiologischen Verfahren könnten Ärzte neben der histo-morphologischen Bewertung auch genetische Variationen oder Protein- und Metabolit-Expressionsprofile nutzen, um eine genauere Risikobewertung vorzunehmen und die Behandlungsentscheidung zu treffen. Die personalisierten Therapien können die klinische Versorgung verbessern durch Vermeidung unnötiger Behandlungen und verringerte Dauer und Kosten des Krankenhausaufenthalts. Daher besteht ein starker Bedarf an zuverlässigeren molekularen Biomarker Profilen. In dieser Dissertation wird ein neuartiges Verfahren, die sogenannten bildgebenden Massenspektrometrie (MADLI-MSI) eingesetzte um das Potential von räumlich aufgelösten Peptide-Signaturen (direkt aus dem Tumorgewebe; in situ) für (i) die Diskriminierung von Subtypen des serösen Ovarialkarzinom (HGSOC) zu untersuchen und (ii) die Risikoabschätzung des Neuroblastomes. Dabei wurden univariate und multivariate statischer Verfahren eingesetzt, um assoziierten Peptide- Signaturen zu bestimmen. Mittels komplementärer Verfahren, Flüssigkeitschromatographie basierte Massenspektrometrie wurden die korrespondierenden Proteine zu den Peptiden identifiziert und Immunhistologisch verifiziert. Folglich wurden Peptidsignaturen zur Vorhersage des Wiederauftretens der Krankheit bei HGSOC-Patienten im Frühstadium und zur Unterscheidung von Hochrisiko-Neuroblastom Patienten von anderen Risikogruppen identifiziert. Diese Ergebnisse deuten darauf hin, dass die MALDI-MSI-Technik eine vielversprechende Analysemethode ist, die die Diagnose und die Entscheidung über die Behandlung erleichtert. Außerdem hat sie neue biologische Erkenntnisse über die Heterogenität des Tumors geliefert, die der Entwicklung von molekularen Biomarker-Profilen zu Gute kommen könnten. Die Daten dieser Dissertation wurden in der Zeitschrift „Cancers (MDPI)" 2020 und 2021 veröffentlicht

Group Sparse Precoding for Cloud-RAN with Multiple User Antennas

Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, the group sparse beamforming schemes attracts extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assumes that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the $\ell_0$-norm as a penalty and then uses the reweighted $\ell_1$ heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from exhaustive search

Buddhist entrepreneurs, charitable behaviors, and social entrepreneurship : Evidence from China

Acknowledgements We thank the Editor-in-Chief Zoltan J. Acs, David B. Audretsch, the anonymous reviewers, and Shaker A. Zahra and Yong Li for their helpful comments and suggestions.The usual disclaimers apply. Funding The authors acknowledge funding from the National Social Science Foundation of China (grant number: 20AGL008), National Natural Science Foundation of China (grant number: 72172056) and the General Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province (grant number: 2020SJA0254).Peer reviewedPublisher PD

Entrepreneurship Knowledge : When East meets West

Acknowledgements The lead guest editor would like to express his sincerest thanks to Fabian Jintae Froese, for his excellent patience and guidance of this special issue and his thanks to Robert Wuebker, Qunwan Li, Julio de Castro, Chunhua Chen, Song Lin, and Zuhui Xu who provided very useful helps at different stages of the developments of this special issue and when this editorial paper was developed.Peer reviewedPostprin

Previous military experience and entrepreneurship toward poverty reduction : evidence from China

Acknowledgements We thank the Editor-in-Chief Brandon Randolph-Seng, the anonymous reviewers, and Shaker A. Zahra and Yong Li for their helpful comments and suggestions. We acknowledge funding from the General Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province (grant number: 2020SJA0254). The usual disclaimers apply.Peer reviewedPostprin

Strategic ambidexterity and innovation in Chinese multinational vs. indIgenous firms : The role of managerial capability

The authors would like to acknowledge the financial support provided by National Natural Science Foundation of China (No. 71728003) and University of Macau MYRG (Grant Number: 2016-00207-FBA, Grant Number: 2018-00171-FBA) for this research.Peer reviewedPostprin

YATO: Yet Another deep learning based Text analysis Open toolkit

We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://youtu.be/tSjjf5BzfQg