Cooperative phagocytosis underlies macrophage immunotherapy of solid tumours and initiates a broad anti-tumour IgG response

Macrophages are abundant in solid tumours and typically associate with poor prognosis, but macrophage clusters in tumour nests have also been reported as beneficial even though dispersed macrophages would have more contacts with cancer cells. Here, by maximizing both phagocytic activity and macrophage numbers, we discover cooperative phagocytosis by low entropy clusters in rapidly growing engineered immuno-tumouroids. The results fit the calculus of proliferation-versus-engulfment, and rheological measurements and molecular perturbations provide a basis for understanding phagocytic disruption of a tumour's cohesive forces in soft cellular phases. The perturbations underscore the utility of suppressing a macrophage checkpoint in combination with an otherwise ineffective tumour-opsonizing monoclonal antibody, and the approach translates in vivo to tumour elimination that durably protects mice from re-challenge and metastasis. Adoptive transfer of engineered macrophages increases the fraction of mice that eliminate tumours and potentially overcomes checkpoint blockade challenges in solid tumours like insufficient permeation of blocking antibodies and on-target, off-tumour binding. Finally, anti-cancer IgG induced in vivo are tumour-specific but multi-epitope and contribute to a phagocytic feedback that drives macrophage clustering in vitro. Given that solid tumours remain challenging for immunotherapies, durable anti-tumour responses here illustrate unexpected advantages in maximizing net phagocytic activity.

A Thermodynamic Measure of Sustainability

A novel thermodynamic approach to the quantification of the “degree of sustainability” is proposed and discussed. The method includes a rigorous -and innovative- conversion procedure of the so-called externalities that leads to their expression in terms of the exergy of their equivalent primary resources consumption. Such a thermodynamic approach suggests a detailed re-evaluation of the concept of sustainability because it is well-known that the Second Law strictly negates the possibility for any open and evolving system to maintain itself in a “sustainable” state without availing itself of a continuous supply of low-entropy (i.e., high specific exergy) input. If a human society is modeled as an open system, its capacity to “grow sustainably” depends not only on how it uses non-renewable resources, but also on the rate at which it exploits the renewable ones. The necessary inclusion of different forms of energy- and material flows in such an analysis constitutes per se an argument in favor of a resource-based exergy metrics. While it is true that the thermodynamically oriented approach proposed here neglects all of the non-thermodynamic attributes of a “sustainable system” (in the Bruntland sense), it is also clear that it constitutes a rigorous basis on which different physically possible scenarios can be rigorously evaluated. Non-thermodynamic indicators can be still used at a “second level analysis” and maintain their usefulness to indicate which one of the “thermodynamically least unsustainable” scenarios is most convenient from an ethical or socio-economic perspective for the considered community or for the society as a whole. The proposed indicator is known as “Exergy Footprint,” and the advantages of its systematic application to the identification of “sustainable growth paths” is discussed in the Conclusions.

Joint Optimization of Multiprocess Routes and Layout for Low Entropy Flexible Facility

Facility layout is not only the premise of production, but also a breakthrough for manufacturing industry to realize energy saving, environmental protection, and low entropy development. On the one hand, considering the interaction between product process routes and facility layout, a joint optimization model is proposed. The model aims to minimize the total logistics cost and consider the global optimization of facility layout and process route planning. On the other hand, considering the application of low entropy concept in facility layout, the analytic network process (ANP) is used to evaluate the low entropy layout. In the choice of the final facility layout, the algorithm results and expert knowledge are considered comprehensively to make up for the shortcomings of the model in the design of qualitative indicators. The algorithm innovation of this paper is to use genetic algorithm (GA) and particle swarm optimization (PSO) to search the solution of product process routes and facility layout simultaneously, to ensure the overall optimal solution of the two decision variables. Finally, an example is given to compare the joint optimization results with the independent optimization results, and the effectiveness of the joint optimization method is verified.

Intelligent Reflecting Surface Assisted Multi-User Robust Secret Key Generation for Low-Entropy Environments

Channel secret key generation (CSKG), assisted by the new material intelligent reflecting surface (IRS), has become a new research hotspot recently. In this paper, the key extraction method in the IRS-aided low-entropy communication scenario with adjacent multi-users is investigated. Aiming at the problem of low key generation efficiency due to the high similarity of channels between users, we propose a joint user allocation and IRS reflection parameter adjustment scheme, while the reliability of information exchange during the key generation process is also considered. Specifically, the relevant key capability expressions of the IRS-aided communication system is analyzed. Then, we study how to adjust the IRS reflection matrix and allocate the corresponding users to minimize the similarity of different channels and ensure the robustness of key generation. The simulation results show that the proposed scheme can bring higher gains to the performance of key generation.

Two-Dimensional Structure of Flow Channels and Associated Upward Field-Aligned Currents: Model and Observations

Flow bursts are a major component of transport within the plasma sheet and auroral oval (where they are referred to as flow channels), and lead to a variety of geomagnetic disturbances as they approach the inner plasma sheet (equatorward portion of the auroral oval). However, their two-dimensional structure as they approach the inner plasma sheet has received only limited attention. We have examined this structure using both the Rice Convection Model (RCM) and ground-based radar and all sky imager observations. As a result of the energy dependent magnetic drift, the low entropy plasma of a flow burst spreads azimuthally within the inner plasma sheet yielding specific predictions of subauroral polarization stream (SAPS) and dawnside auroral polarization stream (DAPS) enhancements that are related to the field-aligned currents associated with the flow channel. Flow channels approximately centered between the dawn and dusk large-scale convection cells are predicted to give significant enhancements of both SAPS and DAPS, whereas flow channel further toward the dusk (dawn) convection cell show a far more significant enhancement of SAPS (DAPS) than for DAPS (SAPS). We present observations for cases having good coverage of flow channels as they approach the equatorward portion of the auroral oval and find very good qualitative agreement with the above RCM predictions, including the predicted differences with respect to flow burst location. Despite there being an infinite variety of flow channels’ plasma parameters and of background plasma sheet and auroral oval conditions, the observations show the general trends predicted by the RCM simulations with the idealized parameters. This supports that RCM predictions of the azimuthal spread of a low-entropy plasma sheet plasma and its associated FAC and flow responses give a realistic physical description of the structure of plasma sheet flow bursts (auroral oval flow channels) as they reach the inner plasma sheet (near the equatorward edge of the auroral oval).

Predictive Uncertainty Underlies Auditory Boundary Perception

Anticipating the future is essential for efficient perception and action planning. Yet the role of anticipation in event segmentation is understudied because empirical research has focused on retrospective cues such as surprise. We address this concern in the context of perception of musical-phrase boundaries. A computational model of cognitive sequence processing was used to control the information-dynamic properties of tone sequences. In an implicit, self-paced listening task ( N = 38), undergraduates dwelled longer on tones generating high entropy (i.e., high uncertainty) than on those generating low entropy (i.e., low uncertainty). Similarly, sequences that ended on tones generating high entropy were rated as sounding more complete ( N = 31 undergraduates). These entropy effects were independent of both the surprise (i.e., information content) and phrase position of target tones in the original musical stimuli. Our results indicate that events generating high entropy prospectively contribute to segmentation processes in auditory sequence perception, independently of the properties of the subsequent event.

DC-FUDA: Improving Deep Clustering via Fully Unsupervised Domain Adaptation

<div>By transferring knowledge from a source domain, the performance of deep clustering on an unlabeled target domain can be improved. When achieving this, traditional approaches make the assumption that adequate amount of labeled data is available in a source domain. However, this assumption is usually unrealistic in practice. The source domain should be carefully selected to share some characteristics with the target domain, and it can not be guaranteed that rich labeled samples are always available in the selected source domain.</div><div>We propose a novel framework to improve deep clustering by transferring knowledge from a source domain without any labeled data. To select reliable instances in the source domain for transferring, we propose a novel adaptive threshold algorithm to select low entropy instances. To transfer important features of the selected instances, we propose a feature-level domain adaptation network (FeatureDA) which cancels unstable generation process. With extensive experiments, we validate that our method effectively improves deep clustering, without using any labeled data in the source domain. Besides, without using any labeled data in the source domain, our method achieves competitive results, compared to the state-of-the-art methods using labeled data in the source domain.</div>