Dendritic-Cell (DC)-Based Immunotherapy: Tumor Endothelial Marker 8 (TEM8) Gene Expression of DC Vaccines Correlates with Clinical Outcome

ABSTRACT\ud Previous studies have shown that tumor-endothelial markers (TEMs) are upregulated in immunosuppressive, pro-angiogenic dendritic cells (DCs) found in tumor microenvironments. \ud We reported that pro-angiogenic monocyte-derived DCs (Mo-DCs), utilized for therapeutic vaccination of cancer patients upon maturation, markedly differ in their ability to up-regulate tumor-endothelial marker 8 (TEM8) gene\ud expression. A DC vaccination trial of 17 advanced cancer patients (13 melanoma and 4 renal cell carcinoma), carried out at the Cancer Institute of Romagna (I.R.S.T.) in Meldola, highlighted a significant correlation between delayed-type hypersensitivity test (DTH) and overall survival (OS). In the study, relative TEM8 mRNA and protein expression levels (mature (m) vs. immature (i) DCs), in DCs obtained for therapeutic vaccines were evaluated by quantitative real-time RT-PCR and cytofluorimetric analysis, respectively. mDCs from six healthy donors were included for comparison purposes. Eight non-progressing patients, all DTH-positive, had a mean fold increase\ud (mfi) of 1.97 in TEM8 expression. Similarly, a TEM8 mRNA mfi = 2.7 was found in healthy donor mDCs. Conversely, mDCs from nine progressing patients, all but one with negative DTH, had a TEM8 mRNA mfi of 12.88. Thus, mDC TEM8 expression levels would seem to identify (p = 0.0018) patients who could benefit from DC therapeutic vaccination

Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications

In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the userss trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower localisation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Soil and forest regeneration after different extraction methods in coppice forests

Coppice is considered the oldest form of sustainable forest management in the Mediterranean area. Generally, it produces rapidly woody biomass and environmental benefits. This research was implemented through an experimental design based on two steps: analyzing the impact of silvicultural treatment (coppice with standards) and logging on forest soil and tree regeneration. It included the soil and regeneration recovery capacity of forests managed as coppice related to different logging systems and treatments applied over a six-year period. The findings demonstrated that tree species regeneration composition was not affected by silvicultural treatment and only slightly by harvesting system. Instead, the physical, chemical and biological soil features were only marginally affected by the silvicultural treatment applied, but strongly impacted by harvesting operations, with clear differences between the systems. The least damaging harvesting system was TLS (Tree Length System) followed by FTS (Full Tree System) and SWS (Short Wood System) that showed a more intense impact. This trend started only six months after harvesting and continued for more than 36 months post-harvesting in a lesser dynamic. The recovery of coppicing was almost complete 36 months after harvesting, without substantial differences between logging systems. Recovery from logging showed a clear positive trend, but 52 months after harvesting only the TLS area had completely recovered. For FTS and SWS, recovery occurred but was very weak

The ActiveCrowdToolkit: an open-source tool for benchmarking active learning algorithms for crowdsourcing research

We present an open-source toolkit that allows the easy comparison of the performance of active learning methods over a series of datasets. The toolkit allows such strategies to be constructed by combining a judgement aggregation model, task selection method and worker selection method. The toolkit also provides a user interface which allows researchers to gain insight into worker performance and task classification at runtime

Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity

We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the re- porting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application

Efficient Budget Allocation with Accuracy Guarantees for Crowdsourcing Classification Tasks

In this paper we address the problem of budget allocation for redundantly crowdsourcing a set of classification tasks where a key challenge is to find a trade–off between the total cost and the accuracy of estimation. We propose CrowdBudget, an agent–based budget allocation algorithm, that efficiently divides a given budget among different tasks in order to achieve low estimation error. In particular, we prove that CrowdBudget can achieve at most max { 0, K/2 - O (√B) } estimation error with high probability, where K is the number of tasks and B is the budget size. This result significantly outperforms the current best theoretical guarantee from Karger et al. In addition, we demonstrate that our algorithm outperforms existing methods by up to 40% in experiments based on real–world data from a prominent database of crowdsourced classification responses

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

Corsican pine (Pinus laricio Poiret) stand management: Medium and long lasting effects of thinning on biomass growth

With the aim of acquiring better comprehension of the ecological and productive aspects of the management of pine forests, we monitored logging damage and evaluated the effects of thinning on stand growth 20 years after the treatment in a Pinus laricio Poiret stand in central Italy. The objectives of the present study were to estimate the injury levels to the remaining trees after thinning; to assess logging damage in the long-term by monitoring residual trees at the end of thinning; to evaluate the effect of damage on the radial growth of trees; to assess the stand dynamics in relation to injury levels and the treatment applied in a twenty-year range; to understand a possible treatment return time; and to evaluate the existence of the "thinning shock". The results were that 20 years after treatment, the stand dynamics showed a complete recovery; logging damage did not affect the radial growth of P. laricio over time; a second treatment seem to be sustainable starting from the fifteenth year after the previous treatment; and the thinning shock can be clearly evaluated in the first six to seven years after the treatment

Coarse woody debris variability due to human accessibility to forest

Originally published in Forests (MDPI): Behjou FK, Lo Monaco A*, Tavankar F, Venanzi R, Nikooy M, Picchio R (2018) Coarse woody debris variability as result of human accessibility to forest. Forests 9(9): article number 509 (open access) Corresponding author: Angela Lo Monaco, [email protected] DOI: 10.3390/f9090509 The article can be dowloaded at: https://www.mdpi.com/1999-4907/9/9/509 Abstract: Coarse woody debris (CWD) plays an important role in supporting biodiversity and assisting ecological processes. Sometimes local people intervene modifying the expected distribution of CWD components, harvested as fuel wood. The effect of the human accessibility (HA) on the volume and characteristics of CWD (snag, downed log and stump) was investigated in the natural uneven-age mixed hardwood stands of the Hyrcanian forests of Iran to quantify the impact on CWD. The HA was classified into three classes (easy, medium and difficult) on the basis of slope class, slope direction to the nearest road and road type. As expected, a negative relationship between the degree of accessibility was found with respect to the main qualitative and quantitative indices referring to CWD. The results showed that the volume of CWD decreased with an increase in human accessibility class (HAC), thus the mean volume of CWD in the difficult, medium and easy accessibility classes were 14.87 m3 ha1, 8.84 m3 ha1 and 4.03 m3 ha1, respectively. The decrease in CWD volume was more associated with the decreasing volume of small diameter of low decayed downed logs. The ratio of snag volume to standing volume, the ratio of downed log volume to the volume of trees and the ratio of CWD volume to standing volume increased with a decrease in HAC, while the ratio of downed log volume to snag volume decreased with a decrease in HAC. No selective behaviour on the botanical species of CWD was recorded. For ecological forest management, the effect of HAC on CWD should be considered. A constant supply of snags and downed logs must be preserved to assure a high level of biodiversity. To balance social needs and biodiversity requirements, an increased level of CWD retention might be needed in areas with easy accessibility. The obtained results may be useful when ecological and socio-economical needs have to be taken into consideration in future policy-making decisions. Keywords: snag; downed log; stump; forest road; uneven-agemixed hardwood stands; Hyrcanian forest