bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nowcasting and Predicting the Kp Index Using Historical Values and Real‐Time Observations
Authors: Shprits Yuri Y. , Vasile Ruggero , Zhelavskaya Irina S. 
Published: July 2019

AbstractCurrent algorithms for the real‐time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind‐based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short‐term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer‐term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.
Progressive Growth of the Cerro Bayo Cryptodome, Chachahuén Volcano, Argentina—Implications for Viscous Magma Emplacement
Authors: Burchardt Steffi , Mattsson Tobias , Palma J. Octavio , Galland Olivier , Almqvist Bjarne , Mair Karen , Jerram Dougal A. , Hammer Øyvind , Sun Yang 
Published: July 2019

AbstractCryptodome and dome collapse is associated with volcanic hazards, such as explosive eruptions, pyroclastic density currents, and volcanic edifice collapse. The study of the growth and evolution of volcanic domes provides vital information on the link between dome growth and the development of weakness zones that may cause collapse. The Cerro Bayo cryptodome is superbly exposed in the eroded Miocene Chachahuén volcano in the Neuquén basin, Argentina. Cerro Bayo is a >0.3‐km3 trachyandesitic cryptodome that intruded within the uppermost kilometer of the Chachahuén volcano. Here we investigate the emplacement of the Cerro Bayo cryptodome using structural mapping, photogrammetry and measurement of magma flow indicators, brittle deformation features, and magnetic fabrics with anisotropy of magnetic susceptibility. Magma flow fabrics near the margin are concentric and indicate contact‐parallel flow and internal inflation of the body. Magmatic and magnetic fabrics and fracture patterns in the interior of the cryptodome are more complex and outline several structural domains. These domains are separated by magmatic shear zones that accommodated intrusion growth. The shear zones locally overprint the earlier formed concentric fabric. The nature of the structural domains shows that the emplacement of Cerro Bayo occurred in three stages that resemble the endogenous to exogenous growth of volcanic domes. The formation of magmatic shear zones during cryptodome formation may have a profound effect on cryptodome stability by creating weakness zones that increase the risk of collapse.
Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data
Authors: Mazzotti Giulia , Currier William Ryan , Deems Jeffrey S. , Pflug Justin M. , Lundquist Jessica D. , Jonas Tobias 
Published: July 2019

AbstractThe retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.
On the Effect of Nonlinear Recessions on Low Flow Variability: Diagnostic of an Analytical Model for Annual Flow Duration Curves
Authors: Karst Nathaniel , Dralle David , Müller Marc F. 
Published: July 2019

AbstractPredicting the proportion of the water year a given stream will remain at or above various flow thresholds is critically important for making sound water management decisions. Flow duration curves (FDCs) succinctly capture this information using all data available over some historical period, while annual flow duration curves (AFDCs) instead use data from each individual water year. Analyzing the population of AFDCs, and in particular the tails of this distribution, can allow water managers to better prepare for years with extreme streamflow conditions. However, long time series of observations are necessary to capture interannual streamflow variations and are problematic to obtain in rapidly changing and poorly gauged catchments. By incorporating a process‐based model to construct AFDCs based on daily rainfall statistics and flow recession characteristics, the proposed approach is a first step toward addressing this challenge. Results indicate that prediction performance varies substantially across flow quantiles and that the current model fails to properly capture the interannual variability of low flows. Numerical analyses attributed these errors to nonlinearity in storage‐discharge relation, rather than cross‐scale streamflow correlations and non‐Poissonian rainfall, explaining the origin of commonly observed heavy‐tailed behavior in low flow quantiles. We present a case study on hydroelectric power generation, showing that faithfully capturing both interannual streamflow variability and recession nonlinearity has important implications for installation profitability.
High‐Resolution Snowline Delineation From Landsat Imagery to Infer Snow Cover Controls in a Himalayan Catchment
Authors: Girona‐Mata Marc , Miles Evan S. , Ragettli Silvan , Pellicciotti Francesca 
Published: July 2019

AbstractThe snow cover dynamics of High Mountain Asia are usually assessed at spatial resolutions of 250 m or greater, but this scale is too coarse to clearly represent the rugged topography common to the region. Higher‐resolution measurement of snow‐covered area often results in biased sampling due to cloud cover and deep shadows. We therefore develop a Normalized Difference Snow Index‐based workflow to delineate snow lines from Landsat Thematic Mapper/Enhanced Thematic Mapper+ imagery and apply it to the upper Langtang Valley in Nepal, processing 194 scenes spanning 1999 to 2013. For each scene, we determine the spatial distribution of snow line altitudes (SLAs) with respect to aspect and across six subcatchments. Our results show that the mean SLA exhibits distinct seasonal behavior based on aspect and subcatchment position. We find that SLA dynamics respond to spatial and seasonal trade‐offs in precipitation, temperature, and solar radiation, which act as primary controls. We identify two SLA spatial gradients, which we attribute to the effect of spatially variable precipitation. Our results also reveal that aspect‐related SLA differences vary seasonally and are influenced by solar radiation. In terms of seasonal dominant controls, we demonstrate that the snow line is controlled by snow precipitation in winter, melt in premonsoon, a combination of both in postmonsoon, and temperature in monsoon, explaining to a large extent the spatial and seasonal variability of the SLA in the upper Langtang Valley. We conclude that while SLA and snow‐covered area are complementary metrics, the SLA has a strong potential for understanding local‐scale snow cover dynamics and their controlling mechanisms.
Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases
Authors: Stephens Christopher , Giménez Heau Joaquin , González-Rosas Camila , Ibarra-Cerdeña Carlos , Sánchez-Cordero Victor 
Published: July 2019

AbstractNetworks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.
Young children do not integrate visual and haptic information
Authors: Gori Monica , Del Viva Michela , Sandini Giulio , Burr David 
Published: July 2019

AbstractSeveral studies have shown that adults integrate visual and haptic information (and information from other modalities) in a statistically optimal fashion, weighting each sense according to its reliability. To date no studies have investigated when this capacity for cross-modal integration develops. Here we show that prior to eight years of age, integration of visual and haptic spatial information is far from optimal, with either vision or touch dominating totally, even in conditions where the dominant sense is far less precise than the other (assessed by discrimination thresholds). For size discrimination, haptic information dominates in determining both perceived size and discrimination thresholds, while for orientation discrimination vision dominates. By eight-ten years, the integration becomes statistically optimal, like adults. We suggest that during development, perceptual systems require constant recalibration, for which cross-sensory comparison is important. Using one sense to calibrate the other precludes useful combination of the two sources.
M. tuberculosis interactome analysis unravels potential pathways to drug resistance
Authors: Raman Karthik , Chandra Nagasuma 
Published: July 2019

AbstractDrug resistance is a major problem for combating tuberculosis. Lack of understanding of how resistance emerges in bacteria upon drug treatment limits our ability to counter resistance. By analysis of the Mycobacterium tuberculosis interactome network, along with drug-induced expression data from literature, we show possible pathways for the emergence of drug resistance. To a curated set of resistance related proteins, we have identified sets of high propensity paths from different drug targets. Many top paths were upregulated upon exposure to anti-tubercular drugs. Different targets appear to have different propensities for the four resistance mechanisms. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', which when simultaneously inhibited with the intended target, is likely to help in combating drug resistance. RecA, Rv0823c, Rv0892 and DnaE1 were the best examples of co-targets for combating tuberculosis. This approach is also inherently generic, likely to significantly impact drug discovery.
Circadian rhythmic kinase CK2α phosphorylates BMAL1 to regulate the mammalian clock
Authors: Tamaru Teruya , Hirayama Jun , Isojima Yasushi , Nagai Katsuya , Norioka Shigemi , Takamatsu Ken , Sassone-Corsi Paolo 
Published: July 2019

AbstractClock proteins govern circadian physiology and their function is regulated by a variety of signaling pathways. Here, we show that p45^PFK^, a previously reported circadian rhythmic kinase, corresponds to CK2[alpha]. Rhythmic phosphorylation of the core clock protein BMAL1 by CK2[alpha] occurs in the suprachiasmatic nuclei (SCN), the mammalian central pacemaker. Circadian BMAL1 phosphorylation controls its nucleocytoplasmic localization. Gene silencing for CK2[alpha] and BMAL1 mutagenesis of a highly conserved CK2 phosphorylation site (Ser 90) result in impaired BMAL1 accumulation in the nucleus and subsequent disruption of clock function. These findings reveal that circadian rhythmic kinase CK2 is an essential regulator of the mammalian circadian system.
A Mathematical Model of a Neuron with Synapses based on Physiology
Authors: Zhang Xiaolin 
Published: July 2019

AbstractThe neuron, when considered as a signal processing device, itsinputs are the frequency of pulses received at the synapses, and its output is the frequency of action potentials generated- in essence, a neuron is a pulse frequency signal processing device. In comparison, electrical devices use either digital or analog signals for communication or processing, and the mathematics behind these subjects is well understood. However, in regards to pulse frequency processing devices, there has not yet been a clear and persuasive mathematical model to describe the functions of neurons. It goes without saying that such a model is very important, not only for understanding neuron and neural system behavior, but also for undeveloped potential applications in industry. This paper proposes a method for obtaining the mathematical relationship between the input and output signals of a neuron based on physiological facts. The proposed method focuses on the currents across the postsynaptic membrane of each synapse, and the key is to recognize that the net charge across the whole membrane of a neuron over each action potential cycle must equal to zero. By analyzing the relationship between the input of a synapse and the currents across the postsynaptic membranes, a dynamic pulse frequency model of the neuron can be obtained. Here, we show that the transfer function of a neuron depends on the function of thepostsynaptic current of each synapse in resting state, which can be found by detecting the postsynaptic current when a pulse is received at the synapse. The transfer function of a typical neuron generally includes addition and subtraction of feedthrough terms and/or first order lag functions. To focus on the most basic characteristics of a neuron, accommodation, adaptation, learning, etc. are not discussed in this paper.
Identifying Data Sharing in Biomedical Literature
Authors: Piwowar Heather , Chapman Wendy 
Published: July 2019

AbstractMany policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation.
Identifying Data Sharing in Biomedical Literature
Authors: Piwowar Heather , Chapman Wendy 
Published: July 2019

AbstractMany policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to find shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation.
Human metabolic adaptations and prolonged expensive neurodevelopment: A review
Authors: Skoyles John 
Published: July 2019

Abstract1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment.
Bird pollination of Canary Island endemic plants
Authors: Ollerton Jeff , Cranmer Louise , Stelzer Ralph , Sullivan Steve , Chittka Lars 
Published: July 2019

AbstractThe Canary Islands are home to a guild of endemic, threatened bird pollinated plants. Previous work has suggested that these plants evolved floral traits as adaptations to pollination by flower specialist sunbirds, but subsequently they appear to be have co-opted passerine birds as sub-optimal pollinators. To test this idea we carried out a quantitative study of the pollination biology of three of the bird pollinated plants, Canarina canariensis (Campanulaceae), Isoplexis canariensis (Veronicaceae) and Lotus berthelotii (Fabaceae), on the island of Tenerife. Using colour vision models, we predicted the detectability of flowers to bird and bee pollinators. We measured pollinator visitation rates, nectar standing crops, as well as seed set and pollen removal and deposition. These data showed that the plants are effectively pollinated by non-flower specialist passerine birds that only occasionally visit flowers. The large nectar standing crops and extended flower longevities (>10days) of Canarina and Isoplexis suggests that they have evolved bird pollination system that effectively exploits these low frequency non-specialist pollen vectors and is in no way suboptimal. Seed set in two of the three species was high, and was significantly reduced or zero in flowers where pollinator access was restricted. In L. berthelotii, however, no fruit set was observed, probably because the plants were self incompatible horticultural clones of a single genet. We also show that, while all three species are easily detectable for birds, the orange Canarina and the red Lotus (but less so the yellow-orange Isoplexis) should be difficult to detect for insect pollinators without specialised red receptors, such as bumblebees. Contrary to expectations if we accept that the flowers are primarily adapted to sunbird pollination, the chiffchaff (Phylloscopus canariensis) was an effective pollinator of these species.
Effects of Cations and PH on Antimicrobial Activity of Thanatin and s-Thanatin against Escherichia coli ATCC25922 and B. subtilis ATCC 21332
Authors: Wu Guoqiu , Ding Jiaxuan , Li Hui , Li Linxian , Zhao Rui , Fan Xiaobo , Shen Zilong 
Published: July 2019

AbstractThanatin and s-thanatin were insect antimicrobial peptides which have shown potent antimicrobial activities on a variety of microbes. In order to investigate the effect of cations and pH on the activity of these peptides against Gram-negative bacteria and Gram-positive bacteria, the antimicrobial activities of both peptides were studied in increasing concentrations of monovalent cations (K^+^ and Na^+^), divalent cations (Ca^2+^ and Mg^2+^) and H^+^. The NCCLS broth microdilution method showed that both peptides were sensitive to the presence of cations. The divalent cations showed more antagonized effect on the activity against Gram-negative bacteria than the monovalent cations, since the two peptides lost the ability to inhibit bacterial growth at a very low concentration. In addition, the activities of both peptides tested were not significantly affected by pH. Comparing to studies of other antibacterial peptide activities, our data support a hypothesis that positive ions affect the sensitivity to cation peptides.