A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure

Self-supervised Learning for Electroencephalogram: A Systematic Survey

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.Comment: 35 pages, 12 figure

The role of recommendation agents in relationship marketing for mobile commerce and gaming.

Through the years marketers has adopted more and more sophisticated and effective ways of marketing their products to consumers. Today, relationship marketing is widely recognized as one of the most important aspects of contemporary marketing strategies. The focus has shifted from a one-off transaction to building relationships with consumers. Marketers today are also privileged to be able to make use of the continuous improvements in Internet and mobile technology. They help marketers to understand their consumers better, thus gaining better insights into their preferences. As more business transactions take place wirelessly, there is a growing importance in electronic and mobile commerce. Service providers have turned to intelligent recommendation agents to help them better profile their consumers, recommend preferable products and services, and thus develop two-way relationships with the consumers. This provided marketers a competitive edge over their competitors. In this project, we explore the role in which recommendation agents play in the development and marketing of mobile games. Providing better mobile games is one of the many ways mobile phone developers attract and retain their targeted customers.BUSINES

Data for: A cell surface-binding antibody atlas nominates a MUC18-directed antibody-drug conjugate for targeting melanoma

Recent advances in targeted therapy and immunotherapy have substantially improved the treatment of melanoma. However, therapeutic strategies are still needed for unresponsive or treatment-relapsed melanoma patients. To discover antibody-drug conjugate (ADC)-tractable cell surface targets for melanoma, we developed an atlas of melanoma cell surface binding antibodies (pAbs) using a proteome-scale antibody array platform (PETAL). Target identification of pAbs led to development of melanoma cell killing ADCs against LGR6, TRPM1, ASAP1, and MUC18, among others. MUC18 was overexpressed in both tumor cells and tumor-infiltrating blood vessels across major melanoma subtypes, making it a potential dual-compartment and universal melanoma therapeutic target. AMT-253, an MUC18-directed ADC based on topoisomerase I inhibitor exatecan and a self-immolative T moiety, had a higher therapeutic index compared to its microtubule inhibitor-based counterpart and favorable pharmacokinetics and tolerability in monkeys. AMT-253 exhibited MUC18-specific cytotoxicity through DNA damage and apoptosis and a strong bystander killing effect, leading to potent antitumor activities against melanoma cell line and patient-derived xenograft models. Tumor vasculature-targeting by a mouse MUC18-specific antibody-T1000-exatecan conjugate inhibited tumor growth in human melanoma xenografts. Combination therapy of AMT-253 with an anti-angiogenic agent generated higher efficacy than single agent in a mucosal melanoma model. Beyond melanoma, AMT-253 was also efficacious in a wide range of MUC18-expressing solid tumors. Efficient target/antibody discovery in combination with the T moiety-exatecan linker-payload exemplified here may facilitate discovery of new ADC to improve cancer treatment.Funding provided by: National Key Research and Development Program of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100012166Award Number: Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number:PETAL monoclonal antibody arrays (60,000 mAb printed on 2 x slides) were prepared as previously reported. Melanoma cells GAK, HMVII and A375 (1-2x107) suspended in 2 ml 1x PBS were first labeled with DNA dye Syto-62 (ThermoFisher, S11344) at 1:10000 ratio for 10 minutes. Labeled cells were then incubated with PETAL array for 30 minutes at 37°C without shaking (0.5-1x107 cells per array). After a gentle washing step in 1x PBS buffer to remove unbound cells, the slides were scanned by GenePix 4200A (Modecular Device). Images were analyzed by GenePix Pro software (GenePix Pro, RRID:SCR_010969)

Characterization of recombinant Trypanosoma brucei gambiense Translationally Controlled Tumor Protein (r Tbg TCTP) and its interaction with Glossina midgut bacteria

International audienceIn humans, sleeping sickness (i.e. Human African Trypanosomiasis) is caused by the protozoan parasites Trypanosoma brucei gambiense (Tbg) in West and Central Africa, and T. b. rhodesiense in East Africa. We previously showed in vitro that Tbg is able to excrete/secrete a large number of proteins, including Translationally Controlled Tumor Protein (TCTP). Moreover, the tctp gene was described previously to be expressed in Tbg-infected flies. Aside from its involvement in diverse cellular processes, we have investigated a possible alternative role within the interactions occurring between the trypanosome parasite, its tsetse fly vector, and the associated midgut bacteria. In this context, the Tbg tctp gene was synthesized and cloned into the baculovirus vector pAcGHLT-A, and the corresponding protein was produced using the baculovirus Spodoptera frugicola (strain 9) / insect cell system. The purified recombinant protein rTbgTCTP was incubated together with bacteria isolated from the gut of tsetse flies, and was shown to bind to 24 out of the 39 tested bacteria strains belonging to several genera. Furthermore, it was shown to affect the growth of the majority of these bacteria, especially when cultivated under microaerobiosis and anaerobiosis. Finally, we discuss the potential for TC IP to modulate the fly microbiome composition toward favoring trypanosome survival

Additional file 11: Table S8. of Mutational landscapes of tongue carcinoma reveal recurrent mutations in genes of therapeutic and prognostic relevance

Summary of target exome sequencing results for 60 tongue tumor samples in the prevalence set. (XLSX 14 kb

Additional file 5: Figure S1. of Mutational landscapes of tongue carcinoma reveal recurrent mutations in genes of therapeutic and prognostic relevance

Mutation plot summary of 18 oral tongue squamous cell carcinoma patients examined in the ‘discovery set’. The top plot shows the key clinical parameters, below which the mutation status of the recurrently mutated genes for each tumor is indicated. Somatic mutations are colored according to functional class and color coded according to the legend below the plot. Prevalence is indicated as number of mutations in the graph on the right and mutational frequency is given in the left of the mutation plot. (PPT 423 kb

Additional file 7: Figure S2. of Mutational landscapes of tongue carcinoma reveal recurrent mutations in genes of therapeutic and prognostic relevance

a Mutational signatures for each sample in the discovery set. b Mutational signatures found in the discovery set grouped according to clinical characteristics (age, recurrence, gender, ethnicity, and smoking status). Signatures are displayed according to the 96 substitution classification defined by the substitution class and sequence context immediately 3′ and 5′ of the mutated base. The probability bars for the six types of substitutions are displayed in different colors. The mutation types are on the horizontal axes, whereas vertical axes depict the proportions of mutations attributed to a specific mutation type. All mutational signatures are displayed on the basis of the trinucleotide frequency of the human genome. (DOC 913 kb

Additional file 8: Figure S3. of Mutational landscapes of tongue carcinoma reveal recurrent mutations in genes of therapeutic and prognostic relevance

Copy number variation plot summary of 18 oral tongue squamous cell carcinoma patients in the discovery set. The samples are sorted according to smokers and never-smokers as indicated. Regions of gains and losses are color coded as per the legend. (PPT 662 kb