Gas plasma irradiation of breast cancers promotes immunogenicity, tumor reduction, and an abscopal effect in vivo

While many new and emerging therapeutic concepts have appeared throughout the last decades, cancer still is fatal in many patients. At the same time, the importance of immunology in oncotherapy is increasingly recognized, not only since the advent of checkpoint therapy. Among the many types of tumors, also breast cancer has an immunological dimension that might be exploited best by increasing the immunogenicity of the tumors in the microenvironment. To this end, we tested a novel therapeutic concept, gas plasma irradiation, for its ability to promote the immunogenicity and increase the toxicity of breast cancer cells in vitro and in vivo. Mechanistically, this emerging medical technology is employing a plethora of reactive oxygen species being deposited on the target cells and tissues. Using 2D cultures and 3D tumor spheroids, we found gas plasma-irradiation to drive apoptosis and immunogenic cancer cell death (ICD) in vitro, as evidenced by an increased expression of calreticulin, heat-shock proteins 70 and 90, and MHC-I. In 4T1 breast cancer-bearing mice, the gas plasma irradiation markedly decreased tumor burden and increased survival. Interestingly, non-treated tumors injected in the opposite flank of mice exposed to our novel treatment also exhibited reduced growth, arguing for an abscopal effect. This was concomitant with an increase of apoptosis and tumor-infiltrating CD4+ and CD8+ T-cells as well as dendritic cells in the tissues. In summary, we found gas plasma-irradiated murine breast cancers to induce toxicity and augmented immunogenicity, leading to reduced tumor growth at a site remote to the treatment area

Carbon Offsets for South Carolina Family Forest Landowners

Carbon sequestration through recommended forest management practices is an ecosystem service that helps mitigate climate change while generating carbon credits for forestland owners to sell in carbon markets. In this fact sheet, we explained how South Carolina forestland owners can participate in the California’s carbon market in order to preserve forests ecosystems in the state. We believe that this fact sheet would spark interests in discussions about forest offset projects potential in the southern states if published

On the implementation of adaptive sliding mode robust controller in the stabilization of electrically actuated micro-tunable capacitor

Parallel-plates based micro-tunable capacitors are known to have low travel ranges, which deteriorate as going even lower in terms of their initials gap sizes. Such conditions have put strict requirements on the operation of such designs and hence hindering their use in numerous practical applications requiring high tunability. This work is proposed to examine the possibility to implement a closed-loop control strategy to increase the maximum capacitance and therefore tunability of micro tunable capacitors. The suggested control strategy is implemented on an electrostatically actuated parallel-plates (one stationary and one movable) based micro-capacitor and had an objective to stabilize the movable electrode when it is close to the fixed one for the sake of maximizing its maximum capacitance and possibly improving its overall tunability. Robustness of the micro-capacitor to the so-called pull-in phenomenon (short-circuit instability) when using the closed loop control scheme is studied. Indeed, an adaptive sliding mode controller is designed to compensate the effects of uncertainty, disturbance and eliminate any possibility for chattering phenomenon. The controller proficiencies in terms of stabilizing the micro-capacitor and its robustness to uncertainty as well as disturbance have been thoroughly examined. Furthermore, the effects of the control parameters on the behavior of micro-capacitor, such as overshoot, settling time, steady state error, robustness to uncertainty, external disturbances and to the chattering phenomenon, have been completely inspected. The obtained results indicated satisfactory proficiency and trustworthiness of the proposed control strategy to achieve high level of tunability and maximum capacitance

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively

Quantifying Aggravated Threats to Stormwater Management Ponds by Tropical Cyclone Storm Surge and Inundation under Climate Change Scenarios

Stormwater management ponds (SMPs) protect coastal communities from flooding caused by heavy rainfall and runoff. If the SMPs are submerged under seawater during a tropical cyclone (TC) and its storm surge, their function will be compromised. Under climate change scenarios, this threat is exacerbated by sea level rise (SLR) and more extreme tropical cyclones. This study quantifies the impact of tropical cyclones and their storm surge and inundation on South Carolina SMPs under various SLR scenarios. A coupled hydrodynamic model calculates storm surge heights and their return periods using historical tropical cyclones. The surge decay coefficient method is used to calculate inundation areas caused by different return period storm surges under various SLR scenarios. According to the findings, stormwater management ponds will be aggravated by sea level rise and extreme storm surge. In South Carolina, the number of SMPs at risk of being inundated by tides and storm surges increases almost linearly with SLR, by 10 SMPs for every inch of SLR for TC storm surges with all return periods. Long Bay, Charleston, and Beaufort were identified as high-risk coastal areas. The findings of this study indicate where current SMPs need to be redesigned and where more SMPs are required. The modeling and analysis system used in this study can be employed to evaluate the effects of SLR and other types of climate change on SMP facilities in other regions

Quantifying Aggravated Threats to Stormwater Management Ponds by Tropical Cyclone Storm Surge and Inundation under Climate Change Scenarios

Stormwater management ponds (SMPs) protect coastal communities from flooding caused by heavy rainfall and runoff. If the SMPs are submerged under seawater during a tropical cyclone (TC) and its storm surge, their function will be compromised. Under climate change scenarios, this threat is exacerbated by sea level rise (SLR) and more extreme tropical cyclones. This study quantifies the impact of tropical cyclones and their storm surge and inundation on South Carolina SMPs under various SLR scenarios. A coupled hydrodynamic model calculates storm surge heights and their return periods using historical tropical cyclones. The surge decay coefficient method is used to calculate inundation areas caused by different return period storm surges under various SLR scenarios. According to the findings, stormwater management ponds will be aggravated by sea level rise and extreme storm surge. In South Carolina, the number of SMPs at risk of being inundated by tides and storm surges increases almost linearly with SLR, by 10 SMPs for every inch of SLR for TC storm surges with all return periods. Long Bay, Charleston, and Beaufort were identified as high-risk coastal areas. The findings of this study indicate where current SMPs need to be redesigned and where more SMPs are required. The modeling and analysis system used in this study can be employed to evaluate the effects of SLR and other types of climate change on SMP facilities in other regions

Stormwater ponds in coastal South Carolina : 2019 state of knowledge full report

This is a scientific state-of-knowledge report on ponds in coastal South Carolina. This effort consists of an inventory of existing ponds, a comprehensive literature review, gap analysis, and recommendations for outreach. The topics covered in the report include: Inventory and classification of stormwater ponds, as of 2013, in the coastal counties ; Transport of stormwater over surfaces and the function of ponds to retain runoff ; Nature of pollutants in stormwater and the storage ability of ponds ; Ecological function of stormwater ponds within the coastal landscape ; Policy and regulatory lens of coastal stormwater management ; Economic assessment of stormwater management ; Development of a communications strategy towards improved stormwater pond awareness and maintenance