Rentabilidade fisica e econômica de inseticidas para pulgões safra de trigo de 1977.

bitstream/item/84146/1/CNPT-COM.-TEC.-1-77.pd

Human mobility: Models and applications

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordRecent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.US Army Research Offic

Automated robust Anuran classification by extracting elliptical feature pairs from audio spectrograms

This is the autjor accepted manuscript. The final version is available from IEEE via the DOI in this recordEcologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals' mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations. We offer three main contributions. First, we introduce a classification method that has an average accuracy of 86% on a dataset of 736 calls from 48 anuran species from the United States. Our dataset is much larger and diverse than those of previous works on anuran classification. Second, we extract a new type of spectrogram feature that avoids syllable segmentation and the manual cleaning of the recordings. Our method also works with recordings of variable length. Third, our method uses GPS location and a voting scheme to reliably deal with a large number of species and high levels of noise.National Science Foundatio

On the performance of network science metrics as long-term investment strategies in stock markets

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordInternational Conference on Complex Networks and their Applications - COMPLEX NETWORKS 2017: Complex Networks & Their Applications VIFirms and individuals have always searched for investment strategies that perform well and are robust to market variations. Over the years, many strategies have claimed to be effective but few resist the effect of time, that is, most of them become outdated. It turns out that markets have a “self-correcting ability”; the secretive/novel nature of strategies firms employ cannot win forever; other firms eventually implement competing strategies causing the market to adjust. Nowadays, most investment firms “sell” to their clients two approaches: high reward and low reward. Unfortunately the possibility of high reward is generally coupled with low robustness (volatility) and if one wants high robustness the yields are low (low reward). In this paper, we use an approach based on network characteristics extracted from historical market data. Network Science has argued that all complex systems have an underlying network structure that explains the behavior of the system. With this in mind, we propose a long-term investment strategy that builds a network from historical investment data, and considers the current state of this network to decide how to create portfolios. We argue that our approach performs better than standard long-term approaches

P73 in cancer

p73 is a tumor suppressor belonging to the p53 family of transcription factors. Distinct isoforms are transcribed from the p73 locus. The use of 2 promoters at the N-terminus allows the expression of an isoform containing (TAp73) or not containing (ΔNp73) a complete N-terminal transactivation domain, with the latter isoform capable of a dominant negative effect over the former. In addition, both N-terminal variants are alternatively spliced at the C-terminus. TAp73 is a bona fide tumor suppressor, being able to induce cell death and cell cycle arrest; conversely, ΔNp73 shows oncogenic properties, inhibiting TAp73 and p53 functions. Here, we discuss the latest findings linking p73 to cancer. The generation of isoform specific null mice has helped in dissecting the contribution of TA versus ΔNp73 isoforms to tumorigenesis. The activity of both isoforms is regulated transcriptionally and by posttranslational modification. p73 dysfunction, particularly of TAp73, has been associated with mitotic abnormalities, which may lead to polyploidy and aneuploidy and thus contribute to tumorigenesis. Although p73 is only rarely mutated in cancer, the tumor suppressor actions of TAp73 are inhibited by mutant p53, a finding that has important implications for cancer therapy. Finally, we discuss the expression and role of p73 isoforms in human cancer, with a particular emphasis on the neuroblastoma cancer model. Broadly, the data support the hypothesis that the ratio between TAp73 and ΔNp73 is crucial for tumor progression and therapeutic response

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

This is the author accepted manuscriptWe live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures

Differential Diagnosis and Clinical Management of a Case of COVID-19 in a Patient With Stage III Lung Cancer Treated With Radio-chemotherapy and Durvalumab

none14nononeGuerini A.E.; Borghetti P.; Filippi A.R.; Bonu M.L.; Tomasini D.; Greco D.; Imbrescia J.; Volpi G.; Triggiani L.; Borghesi A.; Maroldi R.; Pasinetti N.; Buglione M.; Magrini S.M.Guerini, A. E.; Borghetti, P.; Filippi, A. R.; Bonu, M. L.; Tomasini, D.; Greco, D.; Imbrescia, J.; Volpi, G.; Triggiani, L.; Borghesi, A.; Maroldi, R.; Pasinetti, N.; Buglione, M.; Magrini, S. M

Transcranial direct current stimulation (tDCS) for fatigue in multiple sclerosis

BACKGROUND: The debilitating fatigue that patients with multiple sclerosis (MS) commonly experience during day-to-day living activities responds poorly to current therapeutic options. Direct currents (DC) delivered through the scalp (transcranial DC stimulation or tDCS) at weak intensities induce changes in motor cortical excitability that persist for almost an hour after current offset and depend on current polarity. tDCS successfully modulates cortical excitability in various clinical disorders but no information is available for MS related fatigue. OBJECTIVE: In this study we aimed to assess fatigue symptom after five consecutive sessions of anodal tDCS applied over the motor cortex in patients with MS. METHODS: We enrolled 25 patients with MS all of whom experienced fatigue. We delivered anodal and sham tDCS in random order in two separate experimental sessions at least 1 month apart. The stimulating current was delivered for 15 minutes once a day for 5 consecutive days. In each session the Fatigue Impact Scale (FIS) and the Back Depression Inventory (BDI) were administered before the treatment (baseline), immediately after treatment on day five (T1), one week (T2) and three weeks (T3) after the last tDCS session. RESULTS: All patients tolerated tDCS well without adverse events. The fatigue score significantly decreased after anodal tDCS in 65% of the patients (responders). After patients received tDCS for 5 days their FIS scores improved by about 30% and the tDCS-induced benefits persisted at T2 and T3. CONCLUSION: Our preliminary findings suggest that anodal tDCS applied over the motor cortex, could improve fatigue in most patients with MS. \ua9 2014-IOS Press and the authors. All rights reserved