Role of tumor-derived extracellular vesicles in immunosuppression in malignant melanoma patients

Das Ziel dieser Studie war es die Rolle von extrazellulären Vesikeln (EVs) bei der Umwandlung von zirkulierenden CD14+ Monozyten in monozytäre myeloide suppressor Zellen (M-MDSCs) zu untersuchen. Dabei wurden EVs aus humanen Melanomzelllinien, sowie Plasma von Melanompatienten isoliert. Wir haben gezeigt, dass EVs von HT-144 Zellen (HT-144 EVs) Bcl-2 in CD14+ Monozyten hochregulierten (auf mRNA -und Proteinebene) und dieses eine anti-apoptotische Wirkung hatten. Darüber hinaus zeigten CD14+ Monozyten ein verstärktes Entzündungsprofil, sowie eine verstärkte Migration nach der Stimulation mit HT-144 EVs. Nach der Behandlung der Monozyten mit EVs aus HT-144 und SK-MEL-28 Zellen, wurde ein Hochregulation von PD-L1 und eine herunter Regulierung von HLA-DR auf Monozyten beobachtet. Dieser Phänotyp bestätigte die Konvertierung von klassischen Monozyten zu M-MDSCs. Die stimulierten Monozyten zeigten eine starke immunsuppressive Aktivität, da diese die Proliferation von CD8+ T Zellen, sowie deren Produktion von IFN-ү unterdrückten. Die Hochregulation von PD-L1 wurde über den Toll-like-Rezeptor (TLR)2 und TLR4 Signalweg induziert, wobei TLR4 eine dominantere Rolle spielte. Der Signalweg aktivierte NF-κB, was zur Hochregulation von PD-L1 führte. Die Blockade von TLR4 mittels TLR4-blockierenden Antikörpern, sowie NF-κB mit NF-κB Inhibitoren verringerte die PD-L1 Expression signifikant. Des Weiteren fanden wir, dass auf EVs von Melanomzelllinien HSP86 exprimiert wurde. Der Vergleich von HSP86+ EVs mit HSP86low/- EVs verdeutlichte die Bedeutung von der HSP86 vermittelten PD-L1 Expression und der immunsuppressiven Aktivität. Zudem haben wir die Expression von HSP86 in Plasma EVs von Melanompatienten getestet, welche eine Anti-PD-1 Therapie erhielten. Wir haben gezeigt, dass EVs von Nicht-Respondern die PD-L1 Expression hochregulierten und eine immunsuppressive Aktivität in zirkulierenden Monozyten induzierten. Diese EVs zeigten zusätzlich eine deutlich höhere Expression von HSP86 im Vergleich zu EVs von Respondern. Schließlich untersuchten wir PBMCs von 30 Melanompatienten vor und nach einer Anti-PD-1 Behandlung. Wir fanden eine signifikante Abnahme der PD-L1-Expression in zirkulierenden Monozyten von Respondern im Vergleich zum Status beim Beginn der Therapie. Darüber hinaus zeigten Patienten mit niedrigerer PD-L1 Expression auf zirkulierenden Monozyten besseres progressionsfreies Überleben. Zusammengefasst demonstrieren unsere Ergebnisse eine bedeutende Rolle von Tumor-EVs bei der Umwandlung von zirkulierender Monozyten in M-MDSCs. Des Weiteren konnten wir zeigen, dass die PD-L1 Expression auf Monozyten bei Melanompatienten, welche sich einer Anti-PD-1 Therapie unterziehen, eine entscheidende Rolle bei der Vorhersage des Therapieerfolgs hat

R&D modes and firm performance in high-tech companies: A research based on cross-boundary ambidexterity and network structures

This paper draws on the cross-boundary ambidexterity theory to propose that four different R&D modes impact firm performance differently and that cooperative network structure moderates the above relationships. The theoretical model is tested by using financial and patent data of 587 high-tech firms for 10 consecutive years in China. We find that different R&D modes have different impacts on a firm’s financial and innovative performance, and network structure plays different moderating roles. Practically, this work guides high-tech enterprises to optimize their resource allocation, select the most appropriate R&D mode, and establish efficient cooperative networks

An Augmented Subspace Based Adaptive Proper Orthogonal Decomposition Method for Time Dependent Partial Differential Equations

In this paper, we propose an augmented subspace based adaptive proper orthogonal decomposition (POD) method for solving the time dependent partial differential equations. By augmenting the POD subspace with some auxiliary modes, we obtain an augmented subspace. We use the difference between the approximation obtained in this augmented subspace and that obtained in the original POD subspace to construct an error indicator, by which we obtain a general framework for augmented subspace based adaptive POD method. We then provide two strategies to obtain some specific augmented subspaces, the random vector based augmented subspace and the coarse-grid approximations based augmented subspace. We apply our new method to two typical 3D advection-diffusion equations with the advection being the Kolmogorov flow and the ABC flow. Numerical results show that our method is more efficient than the existing adaptive POD methods, especially for the advection dominated models.Comment: 28 pages, 4 figures, 7 table

Synapse: Interactive Guidance by Demonstration with Trial-and-Error Support for Older Adults to Use Smartphone Apps

As smartphones are widely adopted, mobile applications (apps) are emerging to provide critical services such as food delivery and telemedicine. While bring convenience to everyday life, this trend may create barriers for older adults who tend to be less tech-savvy than young people. In-person or screen sharing support is helpful but limited by the help-givers' availability. Video tutorials can be useful but require users to switch contexts between watching the tutorial and performing the corresponding actions in the app, which is cumbersome to do on a mobile phone. Although interactive tutorials have been shown to be promising, none was designed for older adults. Furthermore, the trial-and-error approach has been shown to be beneficial for older adults, but they often lack support to use the approach. Inspired by both interactive tutorials and trial-and-error approach, we designed an app-independent mobile service, \textit{Synapse}, for help-givers to create a multimodal interactive tutorial on a smartphone and for help-receivers (e.g., older adults) to receive interactive guidance with trial-and-error support when they work on the same task. We conducted a user study with 18 older adults who were 60 and over. Our quantitative and qualitative results show that Synapse provided better support than the traditional video approach and enabled participants to feel more confident and motivated. Lastly, we present further design considerations to better support older adults with trial-and-error on smartphones

Understanding the role of rock heterogeneity in controlling fault strength and stability

The rock heterogeneity exists widely in fault zones; however, the intrinsic mechanism of how it affects the mechanical behavior of faults is poorly understood. To develop a quantitative understanding of the effect of the rock heterogeneity on the strength and stability of faults, here we investigate a pore-pressure model based on rate- and-state friction in the manner of two-degree-of-freedom spring-sliders and analyze the reasons of fault weakening and the conditions of frictional instability by carrying out nonlinear simulations and a linear stability analysis. We find that the strength of heterogeneous faults depends largely on the compaction difference (or differential compaction) between the two gouges (e.g. quartz and clay), and the stability is affected by the proportion of the two gouges patches. Our model implies that the rock heterogeneity is likely to weaken faults and reduce the stability of faults

Recent progress on the unified theory of polarization and coherence for stochastic electromagnetic fields

All optical fields undergo random fluctuation and the underlying theory referred to as coherence and polarization of optical fields has played a fundamental role as an important manifestation of the random fluctuations of the electric fields. In this paper, we reviewed our recent theoretical and experimental work on the unified theory of polarization and coherence including coherence tensor wave, degree of coherence tensor, degree of generalized Stokes parameters, and their applications including coherence tensor holography and two-point resolution of polarimetric imaging

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR)

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results