bluuebunny/crossref_metadata_2025 huggingface dataset:
Decadal Oscillation in the Predictability of Palmer Drought Severity Index in California
Authors: Diodato Nazzareno , De Guenni Lelys Bravo , Garcia Mariangel , Bellocchi Gianni
Published: January 2019

Severity of drought in California (U.S.) varies from year-to-year and is highly influenced by precipitation in winter months, causing billion-dollar events in single drought years. Improved understanding of the variability of drought on decadal and longer timescales is essential to support regional water resources planning and management. This paper presents a soft-computing approach to forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly data covering more than two centuries (1801–2014) was used for the design of ensemble projections to understand and quantify the uncertainty associated with interannual-to-interdecadal predictability. With a predictable structure elaborated by exponential smoothing, the projections indicate for the horizon 2015–2054 a weak increase of drought, followed by almost the same pace as in previous decades, presenting remarkable wavelike variations with durations of more than one year. Results were compared with a linear transfer function model approach where Pacific Decadal Oscillation and El Niño Southern Oscillation indices were both used as input time series. The forecasted pattern shows that variations attributed to such internal climate modes may not provide more reliable predictions than the one provided by purely internal variability of drought persistence cycles, as present in the PDSI time series.
Studies on Synthesis and Characterization of Aqueous Hybrid Silicone-Acrylic and Acrylic-Silicone Dispersions and Coatings. Part I
Authors: Kozakiewicz Janusz , Trzaskowska Joanna , Domanowski Wojciech , Kieplin Anna , Ofat-Kawalec Izabela , Przybylski Jarosław , Woźniak Monika , Witwicki Dariusz , Sylwestrzak Krystyna
Published: January 2019

The objective of the study was to investigate the effect of the method of synthesis on properties of aqueous hybrid silicone-acrylic (SIL-ACR) and acrylic-silicone (ACR-SIL) dispersions. SIL-ACR dispersions were obtained by emulsion polymerization of mixtures of acrylic and styrene monomers (butyl acrylate, styrene, acrylic acid and methacrylamide) of two different compositions in aqueous dispersions of silicone resins synthesized from mixtures of silicone monomers (octamethylcyclotetrasiloxane, vinyltriethoxysilane and methyltriethoxysilane) of two different compositions. ACR-SIL dispersions were obtained by emulsion polymerization of mixtures of the same silicone monomers in aqueous dispersions of acrylic/styrene copolymers synthesized from the same mixtures of acrylic and styrene monomers, so the compositions of ACR and SIL parts in corresponding ACR-SIL and SIL-ACR hybrid dispersions were the same. Examination of the properties of hybrid dispersions (particle size, particle structure, minimum film forming temperature, Tg of dispersion solids) as well as of corresponding coatings (contact angle, water resistance, water vapour permeability, impact resistance, elasticity) and films (tensile strength, elongation at break, % swell in toluene), revealed that they depended on the method of dispersion synthesis that led to different dispersion particle structures and on composition of ACR and SIL part. Generally, coatings produced from hybrid dispersions showed much better properties than coatings made from starting acrylic/styrene copolymer dispersions.
An Illustrated Synoptic Key and Comparative Morphology of the Larvae of Dryophthorinae (Coleoptera, Curculionidae) Genera with Emphasis on the Mouthparts
Authors: Chamorro M.
Published: January 2019

This study provides an illustrated synoptic key and comparative morphology to the 38 known larvae of dryophthorine genera representing seven subtribes in four of the five tribes: Cactophagus LeConte, Cosmopolites Chevrolat, Cyrtotrachelus Schoenherr, Diathetes Pascoe, Diocalandra Faust, Dryophthoroides Roelofs, Dryophthorus Germar, Dynamis Chevrolat, Eucalandra Faust, Eugnoristus Schoenherr, Foveolus Vaurie, Mesocordylus Lacordaire, Metamasius Horn, Metamasius (=Paramasius Kuschel), Myocalandra Faust, Nassophasis Waterhouse, Nephius Pascoe, Odoiporus Chevrolat, Phacecorynes Schoenherr, Polytus Faust, Poteriophorus Schoenherr, Rhabdoscelus Marshall, Rhinostomus Rafinesque, Rhodobaenus LeConte, Rhynchophorus Herbst, Scyphophorus Schoenherr, Sipalinus Marshall, Sitophilus Schoenherr, Sparganobasis Marshall, Sphenophorus Schoenherr, Stenommatus Wollaston, Temnoschoita Chevrolat, Trigonotarsus Guerin-Meneville, Trochorhopalus Kirsch, Tryphetus Faust, Xerodermus Lacordaire, and Yuccaborus LeConte. Only Prodioctes Pascoe was not included due to lack of specimens to examine. Seven genera are reported here for the first time. Detailed line drawings of the mouthparts of 37 genera are provided. The synoptic key is a multi-entry key, different from a traditional, single entry dichotomous key, which allows the user to identify dryophthorine larvae using any combination of characters (couplets). A total of 52 characters are included. This study provides support for the retention of Stromboscerini in the subfamily.
Evaluation of Two Shaping Systems and Two Ultrasonic Irrigation Devices in Removing Root Canal Filling Material from Mesial Roots of Mandibular Molars: A Micro CT Study
Authors: Kaloustian Marc Krikor , Nehme Walid , El Hachem Claire , Zogheib Carla , Ghosn Nabil , Michetti Jérôme , Naaman Alfred , Diemer Franck
Published: January 2019

We assessed the efficiency of two shaping file systems and two passive ultrasonic irrigation (PUI) devices for removing filling material during retreatment. The mesial canals from 44 extracted mandibular molars were prepared and obturated. The teeth were randomly divided into two groups, and then one group was retreated with Reciproc R25 (VDW, Munich, Germany) (n = 44) and the other group was retreated with 2Shape (TS, Micro Mega, Besançon, France) (n = 44). A micro-computed tomography (CT) scan was taken before and after the retreatment to assess the volume of the filling material remnants. The teeth were then randomly divided into four groups to test two different PUI devices: Irrisafe (Satelec Acteon Group, Merignac, France) and Endo Ultra (Vista Dental Products, Racine, WI, USA). The teeth in Group A were retreated with 2Shape to test the Endo Ultra (n = 22) device, the teeth in Group B were retreated with 2Shape in order to test the Irrisafe (n = 22) device, the teeth in Group C were retreated with Reciproc to test the Endo Ultra (n = 22) device, and Group D was retreated with Reciproc to test the Irrisafe (n = 22) device. A third micro-CT scan was taken after the retreatment to test the PUIs. The percentage of Gutta-Percha (GP) and sealer removed was 94.75% for TS2 (p < 0.001) and 89.3% for R25 (p < 0.001). The PUI significantly enhanced the removal of the filling material by 0.76% for Group A (p < 0.001), 1.47% for Group B (p < 0.001), 2.61% for Group C (p < 0.001), and by 1.66% for Group D (p < 0.001). 2Shape was more effective at removing the GP and sealer during retreatment (p = 0.018). The supplementary approach with PUI significantly improved filling material removal, with no statistical difference between the four groups (p = 0.106).
Influence of the Maxillary Sinus on the Accuracy of the Root ZX Apex Locator: An Ex Vivo Study
Authors: El Hachem Roula , Wassef Elie , Mokbel Nadim , Abboud Richard , Zogheib Carla , El Osta Nada , Naaman Alfred
Published: January 2019

This study evaluated the accuracy of the Root ZX (J. Morita, Tokyo, Japan) electronic apex locator in determining the working length when palatal maxillary molar roots are in a relationship with the sinus. Seventeen human maxillary molars with vital pulp were scheduled for an extraction and implant placement as part of a periodontal treatment plan. The access cavity was prepared, and a #10 K file (Dentsply Maillefer, Ballaigues, Switzerland) was inserted into the palatal root using the Root ZX apex locator in order to determine the electronic working length (EWL); then, the teeth were extracted. To determine the real working length (RWL), a #10 K file was introduced into the root canal until its tip touched a glass plate. EWL and RWL were compared. Images reconstructed with CBCT (cone beam computerized tomography) revealed that eight palatal roots were related to the maxillary sinus, whereas nine were not. The results showed a significant difference between the EWL and the RWL of the palatal roots related to the sinus (p < 0.001). No significant difference was observed in measurements of roots not in contact with the sinus (p > 0.05). Within the study limitations, the reliability of Root ZX was influenced by the relationship of the roots with the maxillary sinus.
Complexity of Frontal Cortex fNIRS Can Support Alzheimer Disease Diagnosis in Memory and Visuo-Spatial Tests
Authors: Perpetuini David , Chiarelli Antonio M. , Cardone Daniela , Filippini Chiara , Bucco Roberta , Zito Michele , Merla Arcangelo
Published: January 2019

Decline in visuo-spatial skills and memory failures are considered symptoms of Alzheimer’s Disease (AD) and they can be assessed at early stages employing clinical tests. However, performance in a single test is generally not indicative of AD. Functional neuroimaging, such as functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests in an ecological setting to support diagnosis. Indeed, neuroimaging should not alter clinical practice allowing free doctor-patient interaction. However, block-designed paradigms, necessary for standard functional neuroimaging analysis, require tests adaptation. Novel signal analysis procedures (e.g., signal complexity evaluation) may be useful to establish brain signals differences without altering experimental conditions. In this study, we estimated fNIRS complexity (through Sample Entropy metric) in frontal cortex of early AD and controls during three tests that assess visuo-spatial and short-term-memory abilities (Clock Drawing Test, Digit Span Test, Corsi Block Tapping Test). A channel-based analysis of fNIRS complexity during the tests revealed AD-induced changes. Importantly, a multivariate analysis of fNIRS complexity provided good specificity and sensitivity to AD. This outcome was compared to cognitive tests performances that were predictive of AD in only one test. Our results demonstrated the capabilities of fNIRS and complexity metric to support early AD diagnosis.
Parameter Identification of Fractional-Order Discrete Chaotic Systems
Authors: Peng Yuexi , Sun Kehui , He Shaobo , Peng Dong
Published: January 2019

Research on fractional-order discrete chaotic systems has grown in recent years, and chaos synchronization of such systems is a new topic. To address the deficiencies of the extant chaos synchronization methods for fractional-order discrete chaotic systems, we proposed an improved particle swarm optimization algorithm for the parameter identification. Numerical simulations are carried out for the Hénon map, the Cat map, and their fractional-order form, as well as the fractional-order standard iterated map with hidden attractors. The problem of choosing the most appropriate sample size is discussed, and the parameter identification with noise interference is also considered. The experimental results demonstrate that the proposed algorithm has the best performance among the six existing algorithms and that it is effective even with random noise interference. In addition, using two samples offers the most efficient performance for the fractional-order discrete chaotic system, while the integer-order discrete chaotic system only needs one sample.
Monitoring System for Laboratory Mice Transportation: A Novel Concept for the Measurement of Physiological and Environmental Parameters
Authors: González-Sánchez Carlos , Fraile Juan-Carlos , Pérez-Turiel Javier , Damm Ellen , Schneider Jochen G. , Schmitt Daniel , Ihmig Frank R.
Published: January 2019

Laboratory mice are used in biomedical research as “models” for studying human disease. These mice may be subject to significant levels of stress during transportation that can cause alterations that could negatively affect the results of the performed investigation. Here, we present the design and realization of a prototypical transportation container for laboratory mice, which may contribute to improved laboratory animal welfare. This prototype incorporates electric potential integrated circuit (EPIC) sensors, which have been shown to allow the recording of physiological parameters (heart rate and breathing rate) and other sensors for recording environmental parameters during mouse transportation. This allows for the estimation of the stress levels suffered by mice. First experimental results for capturing physiological and environmental parameters are shown and discussed.
Predicting Human Location Using Correlated Movements
Authors: Dao Thi-Nga , Le Duc Van , Yoon Seokhoon
Published: January 2019

This paper aims at estimating the current location, or predicting the next location, of a person when the recent location sequence of that person is unknown. Inspired by the fact that the behavior of an individual is greatly related to other people, a two-phase framework is proposed, which first finds persons who have highly correlated movements with a person-of-interest, then estimates the person’s location based on the position information for selected persons. For the first phase, we propose two methods: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). The CISB method finds persons who have similar encounters with other members in the entire community. In the BSB method, members are selected if they show similar behavioral patterns with a given person, even though there are no direct encounters or evident co-locations between them. For the second phase, a neural network is considered in order to develop the prediction model based on the selected members. Evaluation results show that the proposed prediction model under the BSB scheme outperforms other methods, achieving top-1 accuracy of 71.13% and 69.36% for estimations of current and next locations, respectively, with the MIT dataset and 92.31% and 92.03% in case of the Dartmouth dataset.
Efficient Pilot Decontamination Schemes in 5G Massive MIMO Systems
Authors: Saraereh Omar A. , Khan Imran , Lee Byung Moo , Tahat Ashraf
Published: January 2019

Massive Multiple-input Multiple-output (MIMO) is an emerging technology for the 5G wireless communication systems which has the potential to provide high spectral efficient and improved link reliability and accommodate large number of users. Aiming at the problem of pilot contamination in massive MIMO systems, this paper proposes two algorithms to mitigate it. The first algorithm is depending on the idea of Path Loss to perform User Grouping (PLUG) which divide the users into the center and edge user groups depending on different levels of pilot contamination. It assigns the same pilot sequences to the center users which slightly suffer from pilot contamination and assign orthogonal pilot sequences to the edge users which severely suffer from pilot contamination. It is assumed that the number of users at the edge of each cell is the same. Therefore, to overcome such limitations of PLUG algorithm, we propose an improved PLUG (IPLUG) algorithm which provides the decision parameters for user grouping and selects the number of central and edge users in each cell in a dynamic manner. Thus, the algorithm prevents the wrong division of users in good channel conditions being considered as an edge user which causes large pilot overhead, and also identifies the users with worst channel conditions and prevents the wrong division of such users from the center user group. The second algorithm for pilot decontamination utilizes the idea of pseudo-random codes in which orthogonal pilot are assigned to different cells. Such codes are deployed to get a transmission pilot by scrambling the user pilot in the cell. Since the pilot contamination is generated because different cells multiplex the same set of orthogonal pilots and the pseudo-random sequences have good cross-correlation characteristics, this paper uses this feature to improve the orthogonality of pilots between different cells. Simulation results show that the proposed algorithms can effectively improve channel estimation performance and achievable rate as compared with other schemes.
New Theoretical and Methodological Approaches to the Study of Heat Transfer in Coal Dust Combustion
Authors: Toropov Evgeniy , Osintsev Konstantin , Aliukov Sergei
Published: January 2019

The existing theories of heat transfer in combustion chambers of boiler units fail to take into account a number of important factors that affect the reliability of results, and the methodological approaches to optimizing combustion processes can be revised in view of the spatial and temporal parameters of flame. Hence, the research aimed to improve the fundamental theoretical and methodological principles of studying heat transfer in coal dust combustion in the combustion chambers of industrial steam generators. The authors proposed to extend the theory of heat transfer with the mathematical description of particle size distribution of coal dust. In addition, the authors used the developed mathematical model of coal dust combustion based on a continuous curve of the particle size distribution in the ensemble. The mathematical model is consistent with the aeromechanical and thermal characteristics of flame. This work introduced a concept of flame continuum as a continuous medium, where the processes of combustion and heat transfer are studied. To achieve the research aim, in this paper, the methods of combustion chamber zoning, the equations of stationary and non-stationary heat conduction, radiation, and convective heat transfer, were used. These methods were tested on a number of high-temperature units.
Multi-Level Market Transaction Optimization Model for Electricity Sales Companies with Energy Storage Plant
Authors: Wang Guan , Tan Zhongfu , Lin Hongyu , Tan Qingkun , Yang Shenbo , Ju Liwei , Ren Zhongrui
Published: January 2019

Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.
Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks
Authors: Ameen Bikhtiyar , Balzter Heiko , Jarvis Claire , Wheeler James
Published: January 2019

More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.
A Modified One-Cycle-Control Method for Modular Multilevel Converters
Authors: Tian Xu , Ma Yue , Yu Jintao , Wang Cong , Cheng Hong
Published: January 2019

In this paper, a new One-Cycle-Control (OCC) method is designed for a modular multilevel converter (MMC) based on the principle of the equivalent resistance constant. The proposed controller has a simple structure and a small amount of calculation by cancelling the current inner loop proportional integral (PI) controller and the inverse transform in the traditional direct-quadrature (DQ) control. Compared to the traditional OCC controller, the new one separates the control method from the modulation strategy, making it possible to use not only carrier-based pulse-width modulation (PWM), but also nearest level modulation PWM to generate drive signals. Besides, the independent control of the active and the reactive power is implemented by injecting a reference current with the same phase of the supply voltage or a reference current which lags the supply voltage by π/2 into the controller, so the converter can operate in four quadrants and it can work in either a grid-connect or off-grid environment. The feasibility and the performance of the proposed OCC method have been validated by both the simulation under the MATLAB/SIMULINK (R2012a) environment and experimental results.
Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees
Authors: Cai Long , Gu Jie , Ma Jinghuan , Jin Zhijian
Published: January 2019

With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model. Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014). The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available.
Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series
Authors: Zhang Tianjun , Song Shuang , Li Shugang , Ma Li , Pan Shaobo , Han Liyun
Published: January 2019

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.
Evaluating the Effectiveness of Unmanned Aerial Systems (UAS) for Collecting Thematic Map Accuracy Assessment Reference Data in New England Forests
Authors: Fraser Benjamin T. , Congalton Russell G.
Published: January 2019

Thematic mapping provides today’s analysts with an essential geospatial science tool for conveying spatial information. The advancement of remote sensing and computer science technologies has provided classification methods for mapping at both pixel-based and object-based analysis, for increasingly complex environments. These thematic maps then serve as vital resources for a variety of research and management needs. However, to properly use the resulting thematic map as a decision-making support tool, an assessment of map accuracy must be performed. The methods for assessing thematic accuracy have coalesced into a site-specific multivariate analysis of error, measuring uncertainty in relation to an established reality known as reference data. Ensuring statistical validity, access and time constraints, and immense costs limit the collection of reference data in many projects. Therefore, this research proposes evaluating the feasibility of adopting the low-cost, flexible, high-resolution sensor-capable Unmanned Aerial Systems (UAS, UAV, or Drone) platform for collecting reference data to use in thematic map accuracy assessments for complex environments. This pilot study analyzed 377.57 ha of New England forests, over six University of New Hampshire woodland properties, to compare the similarity between UAS-derived orthomosaic samples and ground-based continuous forest inventory (CFI) plot classifications of deciduous, mixed, and coniferous forest cover types. Using an eBee Plus fixed-wing UAS, 9173 images were acquired and used to create six comprehensive orthomosaics. Agreement between our UAS orthomosaics and ground-based sampling forest compositions reached 71.43% for pixel-based classification and 85.71% for object-based classification reference data methods. Despite several documented sources of uncertainty or error, this research demonstrated that UAS are capable of highly efficient and effective thematic map accuracy assessment reference data collection. As UAS hardware, software, and implementation policies continue to evolve, the potential to meet the challenges of accurate and timely reference data collection will only increase.
Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model
Authors: Fraser Jacob S. , Wang Wen J. , He Hong S. , Thompson Frank R.
Published: January 2019

Fire is a multi-scale process that is an important component in determining ecosystem age structures and successional trajectories across forested landscapes. In order to address questions regarding fire effects over large spatial scales and long temporal scales researchers often employ forest landscape models which can model fire as a spatially explicit disturbance. Within forest landscape models site-level fire effects are often simplified to the species, functional type, or cohort level due to time or computational resource limitations. In this study we used a subset of publicly available U.S. Forest Service forest inventory data (FIA) to estimate short-term fire effects on tree densities across multiple stem diameter classes in two ecological sections in the central and southern United States. We found that FIA plots where low-intensity fires occurred within the preceding five years in the Ozark Highlands ecological section had significantly reduced stem densities in the two smallest diameter classes and in the Gulf Coastal Plains and Flatwoods fire reduced stem densities in the three smallest diameter classes. Using an independent subset of FIA plots we then parameterized and calibrated a forest landscape model to simulate site-level fire effects using a logistic regression based method and compare the results to previous methods of modeling fire effects. When representative landscapes from both study areas were simulated under a low-intensity fire regime using a forest landscape model the logistic regression probability method of modeling fire effects produced a similar reduction in stem densities while the previous age-cohort method overestimated density reductions across diameter classes. A more realistic representation of fire effects, particularly in low intensity fire regimes, increases the utility of forest landscape models as tools for planning and management.
Context-Dependence of Urban Forest Vegetation Invasion Level and Alien Species’ Ecological Success
Authors: Dyderski Marcin K. , Jagodziński Andrzej M.
Published: January 2019

Research Highlights: Urban ecosystems are claimed to be more invaded than natural vegetation. Despite numerous studies, the patterns of alien species occurrence in urban forests are rarely linked to invasion ecology hypotheses. Background and Objectives: We assumed that patterns of invasion level (i.e., neophyte richness) and neophyte ecological success (cover) are context-dependent, i.e., depend on the type of vegetation, and that hypotheses connected with empty niche and biotic acceptance will have the strongest support in urban forests. We also tested biotic resistance, habitat filtering, disturbance, resource availability, and environmental heterogeneity hypotheses. Materials and Methods: Using a random forest algorithm, we tested the importance of factors related to invasion ecology hypotheses in a dataset of urban forest vegetation plots (n = 120). We studied seven types of forest plant communities occurring in Poznań (W Poland) and we assessed the vegetation’s taxonomic and functional composition. Results: We found that models of alien species richness and cover explained 28.5% and 35.0% of variance, respectively. Vegetation type was of the highest importance in both cases, suggesting that the occurrence of alien plant species is context-dependent. Resource availability and disturbance ecological indicator values were also of high importance. Conclusions: Our study supported resource availability and habitat filtering hypotheses as explanations of the level of invasion and ecological success of alien species in an urban forest, with partial support for the disturbance hypothesis. Our study revealed that predictors of invasion level are context-dependent, as patterns of alien species richness and cover differed among vegetation types. We highlight context-dependence of alien species invasion patterns in different vegetation types due to the habitat-forming role of dominant tree species and different availability of resources and disturbance levels, as well as different pools of native species. Thus, prevention and management of biological invasions in urban forests should account for forest vegetation type.
Flammability Characteristics of Animal Fibers: Single Breed Wools, Alpaca/Wool, and Llama/Wool Blends
Authors: Galaska Mary L. , Sqrow Larry D. , Wolf J. Douglas , Morgan Alexander B.
Published: January 2019

Animal protein-based fibers used in textiles often are assumed to have uniform properties independent of source, and yet are different when considering texture, structure, and color. Differences between fibers from animal species have been studied in regard to general flammability behavior, but differences between fibers from breeds of the same species have not been studied. Fibers from two sheep breeds (Jacob, CVM/Romeldale) and two camelids (Alpaca, Llama) were studied for flammability effects on fabrics hand knit from yarns made from these different fibers. A total of five different yarns were studied: 100% Jacob, 100% CVM/Romeldale, 100% Alpaca, 50% Llama/Merino wool, and 50% Alpaca/Merino wool. Flammability was studied with cone calorimeter, microcombustion calorimeter, and vertical flame spread techniques. The results from this limited study demonstrate that there are differences between fibers from different breeds and differences between species, but the differences cannot be easily explained on the basis of inherent heat release or chemistry of the fiber. Sometimes yarn density and the tightness of the knit have more of an effect on self-extinguishment in vertical flame spread tests than does fiber heat release/chemistry. Pure Alpaca fiber, however, displays self-extinguishing behavior and low heat release when subjected to combustion conditions. This may be related to the amount of sulfur in its chemical structure, and its ability to be spun into a yarn which yields a tighter hand-knit density.
Calculation of Critical Water Flow Rates for Wildfire Suppression
Authors: Penney Greg , Habibi Daryoush , Cattani Marcus , Carter Murray
Published: January 2019

Predicting water suppression requirements and its impacts on firefighting strategies and logistics within the urban environment has been the subject of many previous studies, however the same level of research has yet to be applied in the realm of wildfire suppression. To work towards addressing this knowledge gap, this paper provides guidance for Incident Controllers in relation to critical water flow rates required to extinguish large wildfire across a wide range of forest fuel loads, fire weather and active fire front depths. This is achieved through mathematical empirical analysis of water flow rates required for head fire suppression during 540 simulated wildfires in forest vegetation. This research applies a fire engineering approach to wildfire suppression logistics and deterministically assess the suitability of appliance and aircraft based head fire suppression. The results highlight the limitations of offensive wildfire suppression involving direct head fire attacks by appliances once wildfires attain a quasi-steady state in forest fuels.
Monitoring the Extragalactic High Energy Sky
Authors: Lenain Jean-Philippe
Published: January 2019

Blazars are jetted active galactic nuclei with a jet pointing close to the line of sight, hence enhancing their intrinsic luminosity and variability. Monitoring these sources is essential in order to catch them flaring and promptly organize follow-up multi-wavelength observations, which are key to providing rich data sets used to derive e.g., the emission mechanisms at work, and the size and location of the flaring zone. In this context, the Fermi-LAT has proven to be an invaluable instrument, whose data are used to trigger many follow-up observations at high and very high energies. A few examples are illustrated here, as well as a description of different data products and pipelines, with a focus given on FLaapLUC, a tool in use within the H.E.S.S. collaboration.
The Iron Age Settlement of Terronha (Viana do Castelo, Northwestern Portugal): Analysis of Ceramic and Lithic Materials in Context
Authors: Oliveira Nuno
Published: January 2019

This article’s main objective is to present some results of a study carried out in Castro de Terronha, located in the Union of Cardielos and Serreleis parishes, county and district of Viana do Castelo, North of Portugal. This settlement was discovered and excavated in 2000 by the archeology company Perennia Monumenta under the scientific direction of Francisco Queiroga. The text focuses on the analysis of structures and indigenous ceramic and lithic remains discovered in sector A. The seven circular structures under study correspond to probable housing structures. Three of them have a vestibule attached. These would be about 5–6 m in diameter—a device of reasonable quality. Also found was a section of wall that ends abruptly, adjoining a great outcrop. Most of the ceramic specimens in articulation with the architectures revealed numerous similarities in morphological, technical and decorative terms with that of the Late Iron Age and the beginnings of Romanization in the Cávado Basin River. The set of remains suggests this settlement was involved in subsistence activities, metallurgy and trade.
A Multi-Pollutant Air Quality Health Index (AQHI) Based on Short-Term Respiratory Effects in Stockholm, Sweden
Authors: Olstrup Henrik , Johansson Christer , Forsberg Bertil , Tornevi Andreas , Ekebom Agneta , Meister Kadri
Published: January 2019

In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NOx, O3 and PM10, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM10 with 3.1%, followed by O3 with 2.0%.
Exploring Novel Functions of the Small GTPase Ypt1p under Heat-Shock by Characterizing a Temperature-Sensitive Mutant Yeast Strain, ypt1-G80D
Authors: Kang Chang Ho , Park Joung Hun , Lee Eun Seon , Paeng Seol Ki , Chae Ho Byoung , Chi Yong Hun , Lee Sang Yeol
Published: January 2019

In our previous study, we found that Ypt1p, a Rab family small GTPase protein, exhibits a stress-driven structural and functional switch from a GTPase to a molecular chaperone, and mediates thermo tolerance in Saccharomyces cerevisiae. In the current study, we focused on the temperature-sensitive ypt1-G80D mutant, and found that the mutant cells are highly sensitive to heat-shock, due to a deficiency in the chaperone function of Ypt1pG80D. This defect results from an inability of the protein to form high molecular weight polymers, even though it retains almost normal GTPase function. The heat-stress sensitivity of ypt1-G80D cells was partially recovered by treatment with 4-phenylbutyric acid, a chemical chaperone. These findings indicate that loss of the chaperone function of Ypt1pG80D underlies the heat sensitivity of ypt1-G80D cells. We also compared the proteomes of YPT1 (wild-type) and ypt1-G80D cells to investigate Ypt1p-controlled proteins under heat-stress conditions. Our findings suggest that Ypt1p controls an abundance of proteins involved in metabolism, protein synthesis, cellular energy generation, stress response, and DNA regulation. Finally, we suggest that Ypt1p essentially regulates fundamental cellular processes under heat-stress conditions by acting as a molecular chaperone.
Comprehensive Analysis of the Cadmium Tolerance of Abscisic Acid-, Stress- and Ripening-Induced Proteins (ASRs) in Maize
Authors: Zhang Jie , Zhu Qiusha , Yu Haijuan , Li Liang , Zhang Guoqiang , Chen Xi , Jiang Mingyi , Tan Mingpu
Published: January 2019

In plants, abscisic acid-, stress-, and ripening-induced (ASR) proteins have been shown to impart tolerance to multiple abiotic stresses such as drought and salinity. However, their roles in metal stress tolerance are poorly understood. To screen plant Cd-tolerance genes, the yeast-based gene hunting method which aimed to screen Cd-tolerance colonies from maize leaf cDNA library hosted in yeast was carried out. Here, maize ZmASR1 was identified to be putative Cd-tolerant through this survival screening strategy. In silico analysis of the functional domain organization, phylogenetic classification and tissue-specific expression patterns revealed that maize ASR1 to ASR5 are typical ASRs with considerable expression in leaves. Further, four of them were cloned for testifying Cd tolerance using yeast complementation assay. The results indicated that they all confer Cd tolerance in Cd-sensitive yeast. Then they were transiently expressed in tobacco leaves for subcellular localization analysis and for Cd-challenged lesion assay, continuously. The results demonstrated that all 4 maize ASRs tested are localized to the cell nucleus and cytoplasm in tobacco leaves. Moreover, they were confirmed to be Cd-tolerance genes in planta through lesion analysis in Cd-infiltrated leaves transiently expressing them. Taken together, our results demonstrate that maize ASRs play important roles in Cd tolerance, and they could be used as promising candidate genes for further functional studies toward improving the Cd tolerance in plants.
Druggable Targets in Cyclic Nucleotide Signaling Pathways in Apicomplexan Parasites and Kinetoplastids against Disabling Protozoan Diseases in Humans
Authors: Kaiser Annette
Published: January 2019

Cell signaling in eukaryotes is an evolutionarily conserved mechanism to respond and adapt to various environmental changes. In general, signal sensation is mediated by a receptor which transfers the signal to a cascade of effector proteins. The cyclic nucleotides 3′,5′-cyclic adenosine monophosphate (cAMP) and 3′,5′-cyclic guanosine monophosphate (cGMP) are intracellular messengers mediating an extracellular stimulus to cyclic nucleotide-dependent kinases driving a change in cell function. In apicomplexan parasites and kinetoplastids, which are responsible for a variety of neglected, tropical diseases, unique mechanisms of cyclic nucleotide signaling are currently identified. Collectively, cyclic nucleotides seem to be essential for parasitic proliferation and differentiation. However, there is no a genomic evidence for canonical G-proteins in these parasites while small GTPases and secondary effector proteins with structural differences to host orthologues occur. Database entries encoding G-protein-coupled receptors (GPCRs) are still without functional proof. Instead, signals from the parasite trigger GPCR-mediated signaling in the host during parasite invasion and egress. The role of cyclic nucleotide signaling in the absence of G-proteins and GPCRs, with a particular focus on small GTPases in pathogenesis, is reviewed here. Due to the absence of G-proteins, apicomplexan parasites and kinetoplastids may use small GTPases or their secondary effector proteins and host canonical G-proteins during infection. Thus, the feasibility of targeting cyclic nucleotide signaling pathways in these parasites, will be an enormous challenge for the identification of selective, pharmacological inhibitors since canonical host proteins also contribute to pathogenesis.
Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools
Authors: Yang Qingxia , Wang Yunxia , Zhang Song , Tang Jing , Li Fengcheng , Yin Jiayi , Li Yi , Fu Jianbo , Li Bo , Luo Yongchao , Xue Weiwei , Zhu Feng
Published: January 2019

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein–protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.