Tracking User-movement in Opportunistic Networks to Support Distributed Query-response During Disaster Management

AbstractEffective communication amongst diverse rescue and relief workers is a primary requirement in any disaster management. Since pre-existing communication infrastructure may not be available, the Opportunistic Network framework provides a potential platform for information communication, where individual smart-phones of rescue and relief workers (the nodes) spread across an environment form a disjoint, peer-to-peer network. Here, a source node communicates with a destination node following hop-by-hop, store-wait-forward cycle, since an end-to-end route connecting them never exists. Also, due to mobility and disconnectedness, nodes have scarce or no knowledge about the network topology. However, in the context of disaster management, in order to evaluate the situation, rescue and relief workers often need to generate different field-related queries and the response to those queries must come from other workers in the field. Since source node (generating the query) is not aware of the location of destination node (answering the query) and all nodes are mobile, it is difficult to implement a query-response mechanism. This paper proposes and evaluates a distributed query-response mechanism that enables any node to track approximate location of other rescue and relief workers, which is turn helps to handle query-response operations

Decentralized Multi-Agent Reinforcement Learning with Global State Prediction

Deep reinforcement learning (DRL) has seen remarkable success in the control of single robots. However, applying DRL to robot swarms presents significant challenges. A critical challenge is non-stationarity, which occurs when two or more robots update individual or shared policies concurrently, thereby engaging in an interdependent training process with no guarantees of convergence. Circumventing non-stationarity typically involves training the robots with global information about other agents' states and/or actions. In contrast, in this paper we explore how to remove the need for global information. We pose our problem as a Partially Observable Markov Decision Process, due to the absence of global knowledge on other agents. Using collective transport as a testbed scenario, we study two approaches to multi-agent training. In the first, the robots exchange no messages, and are trained to rely on implicit communication through push-and-pull on the object to transport. In the second approach, we introduce Global State Prediction (GSP), a network trained to forma a belief over the swarm as a whole and predict its future states. We provide a comprehensive study over four well-known deep reinforcement learning algorithms in environments with obstacles, measuring performance as the successful transport of the object to the goal within a desired time-frame. Through an ablation study, we show that including GSP boosts performance and increases robustness when compared with methods that use global knowledge

Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches

Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and DeepLabV3 are Convolutional Neural Networks used for semantic segmentation. While in separate experiments each of these methods have shown promising results, no prior work has comprehensively and systematically compared the approaches on the same large wound image dataset, or more generally compared deep learning vs non-deep learning wound image segmentation approaches. In this paper, we compare the segmentation performance of AHRF and CNN approaches (FCN, U-Net, DeepLabV3) using various metrics including segmentation accuracy (dice score), inference time, amount of training data required and performance on diverse wound sizes and tissue types. Improvements possible using various image pre- and post-processing techniques are also explored. As access to adequate medical images/data is a common constraint, we explore the sensitivity of the approaches to the size of the wound dataset. We found that for small datasets ( \u3c 300 images), AHRF is more accurate than U-Net but not as accurate as FCN and DeepLabV3. AHRF is also over 1000x slower. For larger datasets ( \u3e 300 images), AHRF saturates quickly, and all CNN approaches (FCN, U-Net and DeepLabV3) are significantly more accurate than AHRF

Tumor-associated macrophages: an effective player of the tumor microenvironment

Cancer progression is primarily caused by interactions between transformed cells and the components of the tumor microenvironment (TME). TAMs (tumor-associated macrophages) make up the majority of the invading immune components, which are further categorized as anti-tumor M1 and pro-tumor M2 subtypes. While M1 is known to have anti-cancer properties, M2 is recognized to extend a protective role to the tumor. As a result, the tumor manipulates the TME in such a way that it induces macrophage infiltration and M1 to M2 switching bias to secure its survival. This M2-TAM bias in the TME promotes cancer cell proliferation, neoangiogenesis, lymphangiogenesis, epithelial-to-mesenchymal transition, matrix remodeling for metastatic support, and TME manipulation to an immunosuppressive state. TAMs additionally promote the emergence of cancer stem cells (CSCs), which are known for their ability to originate, metastasize, and relapse into tumors. CSCs also help M2-TAM by revealing immune escape and survival strategies during the initiation and relapse phases. This review describes the reasons for immunotherapy failure and, thereby, devises better strategies to impair the tumor–TAM crosstalk. This study will shed light on the understudied TAM-mediated tumor progression and address the much-needed holistic approach to anti-cancer therapy, which encompasses targeting cancer cells, CSCs, and TAMs all at the same time

Immune evasion by cancer stem cells ensures tumor initiation and failure of immunotherapy

Cancer stem cells (CSCs) are a small subpopulation of cells that drive the formation and progression of tumors. However, during tumor initiation, how CSCs communicate with neighbouring immune cells to overcome the powerful immune surveillance barrier in order to form, spread, and maintain the tumor, remains poorly understood. It is, therefore, absolutely necessary to understand how a small number of tumor-initiating cells (TICs) survive immune attack during (a) the “elimination phase” of “tumor immune-editing”, (b) the establishment of regional or distant tumor after metastasis, and (c) recurrence after therapy. Mounting evidence suggests that CSCs suppress the immune system through a variety of distinct mechanisms that ensure the survival of not only CSCs but also non-stem cancer cells (NSCCs), which eventually form the tumor mass. In this review article, the mechanisms via which CSCs change the immune landscape of the tissue of origin, which contains macrophages, dendritic cells (DCs), myeloid-derived suppressor cells (MDSCs), natural killer (NK) cells, and tumor-infiltrating lymphocytes, in favour of tumorigenesis were discussed. The failure of cancer immunotherapy might also be explained by such interaction between CSCs and immune cells. This review will shed light on the critical role of CSCs in tumor immune evasion and emphasize the importance of CSC-targeted immunotherapy as a cutting-edge technique for battling cancer by restricting communication between immune cells and CSCs

Follow-up analyses to the O3 LIGO-Virgo-KAGRA lensing searches

Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects. This results in distortions of the observed signal which can be used to extract new information about fundamental physics, astrophysics, and cosmology. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some of the follow-up analyses and strategies that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system from such candidate signals by applying them to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. For strongly-lensed candidates, we verify their significance using a background of simulated unlensed events and statistics computed from lensing catalogs. We also look for potential electromagnetic counterparts. In addition, we analyse in detail a candidate for a strongly-lensed sub-threshold counterpart that is identified by a new method. For microlensing candidates, we perform model selection using a number of lens models to investigate our ability to determine the mass density profile of the lens and constrain the lens parameters. We also look for millilensing signatures in one of the lensed candidates. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations.Comment: 34 pages, 27 figure

On Elser's conjecture and the topology of UU-nucleus complex

Dorpalen-Barry et al. proved Elser's conjecture about sign of Elser's number by interpreting them as certain sums of reduced Euler characteristics of an abstract simplicial complex known as $U$-nucleus complex. We prove a conjecture posed by them regarding the homology of $U$-nucleus complex.Comment: 8 pages, 8 figure

Breast cancer stem cells generate immune-suppressive T regulatory cells by secreting TGFβ to evade immune-elimination

Abstract Cancer stem cells (CSCs), being the primary contributors in tumor initiation, metastasis, and relapse, ought to have seminal roles in evasion of immune surveillance. Tumor-promoting CD4+CD25+FOXP3+ T-regulatory cells (Tregs) have been described to abolish host defense mechanisms by impeding the activities of other immune cells including effector T cells. However, whether CSCs can convert effector T cells to immune-suppressive Treg subset, and if yes, the mechanism underlying CSC-induced Treg generation, are limitedly studied. In this regard, we observed a positive correlation between breast CSC and Treg signature markers in both in-silico and immunohistochemical analyses. Mirroring the conditions during tumor initiation, low number of CSCs could successfully generate CD4+CD25+FOXP3+ Treg cells from infiltrating CD4+ T lymphocytes in a contact-independent manner. Suppressing the proliferation potential as well as IFNγ production capacity of effector T cells, these Treg cells might be inhibiting antitumor immunity, thereby hindering immune-elimination of CSCs during tumor initiation. Furthermore, unlike non-stem cancer cells (NSCCs), CSCs escaped doxorubicin-induced apoptosis, thus constituting major surviving population after three rounds of chemotherapy. These drug-survived CSCs were also able to generate CD4+CD25+FOXP3+ Treg cells. Our search for the underlying mechanism further unveiled the role of CSC-shed immune-suppressive cytokine TGFβ, which was further increased by chemotherapy, in generating tumor Treg cells. In conclusion, during initiation as well as after chemotherapy, when NSCCs are not present in the tumor microenvironment, CSCs, albeit present in low numbers, generate immunosuppressive CD4+CD25+FOXP3+ Treg cells in a contact-independent manner by shedding high levels of immune-suppressive Treg-polarizing cytokine TGFβ, thus escaping immune-elimination and initiating the tumor or causing tumor relapse