bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carbonate-hosted microbial communities are prolific and pervasive methane oxidizers at geologically diverse marine methane seep sites
Authors: Marlow Jeffrey J. , Hoer Daniel , Jungbluth Sean P. , Reynard Linda M. , Gartman Amy , Chavez Marko S. , El-Naggar Mohamed Y. , Tuross Noreen , Orphan Victoria J. , Girguis Peter R. 
Published: June 2021

SignificanceMethane is a strong greenhouse gas that plays a key role in Earth’s climate. At methane seeps, large amounts of methane move upward through the seafloor, where microbial communities consume much of it. A full accounting of methane’s sources and sinks has evaded researchers—in part, perhaps, because key habitats including carbonate rock mounds have been largely neglected. We sampled seven methane seeps representing four geological settings and found that all sites had rock-hosted microbes capable of consuming methane; in lab-based incubations, some did so at the highest rates reported to date. We demonstrate several factors that help determine a sample’s methane-consuming potential and propose that carbonate rocks at methane seeps may represent a methane sink of far-reaching importance.
Epigenetic inheritance of DNA methylation changes in fish living in hydrogen sulfide–rich springs
Authors: Kelley Joanna L. , Tobler Michael , Beck Daniel , Sadler-Riggleman Ingrid , Quackenbush Corey R. , Arias Rodriguez Lenin , Skinner Michael K. 
Published: June 2021

SignificanceEnvironmental factors can promote phenotypic variation through alterations in the epigenome and mediate adaptation of an organism to the environment. Observations suggest the adaptation ofPoecilia mexicanafish to toxic, hydrogen sulfide–rich environments in southern Mexico may, in part, be promoted through epigenetic DNA methylation alterations that became generationally stable and are inherited to subsequent generations independent of the environment. Environmental epigenetics may provide an important mechanism mediating adaptation in this species. This is an observation that the epigenome is stably inherited generationally through the germline after the removal of an environmental stressor (i.e., hydrogen sulfide) from a wild population.
Validating model-based Bayesian integration using prior–cost metamers
Authors: Sohn Hansem , Jazayeri Mehrdad 
Published: June 2021

SignificanceOver the last decade, Bayesian modeling has emerged as a unified approach for capturing a wide range of behaviors such as perceptual illusions, cognitive decision making, and motor control. Despite its remarkable success, the Bayesian approach has been challenged by the issue of model identifiability; that is, the components of a Bayesian model (e.g., prior and cost function) are not uniquely determined by the behavior. Here, we create an experimental paradigm that provides a way to adjudicate whether and when humans build and use the internal models of priors and cost functions. Our work provides a path toward resolving the controversies surrounding the notion of the Bayesian brain.
Restriction of SARS-CoV-2 replication by targeting programmed −1 ribosomal frameshifting
Authors: Sun Yu , Abriola Laura , Niederer Rachel O. , Pedersen Savannah F. , Alfajaro Mia M. , Silva Monteiro Valter , Wilen Craig B. , Ho Ya-Chi , Gilbert Wendy V. , Surovtseva Yulia V. , Lindenbach Brett D. , Guo Junjie U. 
Published: June 2021

Significance
A large variety of RNA viruses, including the novel coronavirus SARS-CoV-2, contain specific RNA structures that promote programmed ribosomal frameshifting (PRF) to regulate viral gene expression. From a high-throughput compound screen, we identified a PRF inhibitor for SARS-CoV-2 and found that it substantially impeded viral replication in cultured cells. Interestingly, the compound could target not only SARS-CoV-2 but also other coronaviruses that use similar RNA structures to promote frameshifting. These results suggest targeting PRF is a plausible, effective, and broad-spectrum antiviral strategy for SARS-CoV-2 and other coronaviruses.
AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2
Authors: Ye Sheng , Zhang Guozhen , Jiang Jun 
Published: June 2021

Significance
The COVID-19 caused by SARS-CoV-2 virus has posed a tremendous threat to human health. The interactions between human angiotensin-converting enzyme 2 and the spike glycoprotein of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines. However, the simulation of these interactions in fluctuating surroundings is challenging because it requires many electronic structure calculations at the quantum mechanics level for a large number of representative configurations. We report a machine learning protocol that can efficiently predict the IR spectra of SARS-CoV-2 with high efficiency and characterize fine changes in IR spectra associated with variations of protein secondary structures. Machine learning provides a cost-effective tool for monitoring of real-time interactions between the SARS-CoV-2 and human ACE2.
Attention, awareness, and the right temporoparietal junction
Authors: Wilterson Andrew I. , Nastase Samuel A. , Bio Branden J. , Guterstam Arvid , Graziano Michael S. A. 
Published: June 2021

SignificanceWe all intuitively know what it is to be subjectively aware of something. For decades, psychologists have also studied the brain’s mechanisms of directing attention. But awareness and attention are not the same and can be separated. How do they relate? Here, we show that a part of the brain, the right temporoparietal junction, may be involved in a specific relationship in which awareness of an item permits the control of attention on that item.
From controversy to complexity
Authors: Dam Helle V. 
Published: June 2021

Abstract
A replication of previous research, this study sets out to re-examine language choice in note-taking for
consecutive conference interpreting – a topic that is widely believed to be subject to conflicting evidence. Extending the
existing database considerably, the study draws on data from seven different consecutive interpreting tasks involving five
different languages and both interpreting directions (B into A and A into B) performed by ten participants with seven different
language combinations. Interpreters’ notes from these performances served as the main data of the study, which was complemented by
questionnaire data to form a mixed-methods design. Analyses of the interpreters’ notes identified the A language as by far the
strongest determinant of language choice, above and beyond other language categories; this confirmed the results of the replicated
research. The questionnaire data, however, did not mirror the patterns found in the interpreting data consistently, suggesting a
complex interplay of behavior and norms. The article concludes with a discussion of these and previous findings, arguing for the
topic of language choice in note-taking to be reframed as one of complexity rather than contradiction.
Active edible coatings in meat
Authors: Forato Lucimara A. , Bernardes-Filho Rubens , Colnago Luiz A. , Nassu Renata T. 
Published: June 2021

Abstract
Meat is a perishable product due to its protein and high-water activity (Aw) and is thus prone to microbial and oxidative processes, which lead to spoilage and undesirable changes in meat physicochemical characteristics. Edible coatings, combined with antioxidant and antimicrobial substances, have been used to improve meat shelf life by creating an oxygen barrier against lipid oxidation, acting as an antimicrobial agent, and to keep meat color. Here, we reviewed the use of active edible coatings and their effects on physicochemical and sensory characteristics of chicken, pork, beef, lamb meat, and fish.
Generalized Chern numbers based on open system Green’s functions
Authors: Farias M Belén , Groenendijk Solofo , Schmidt Thomas L 
Published: June 2021

Abstract
We present an alternative approach to studying topology in open quantum systems, relying directly on Green’s functions and avoiding the need to construct an effective non-Hermitian (nH) Hamiltonian. We define an energy-dependent Chern number based on the eigenstates of the inverse Green’s function matrix of the system which contains, within the self-energy, all the information about the influence of the environment, interactions, gain or losses. We explicitly calculate this topological invariant for a system consisting of a single 2D Dirac cone and find that it is half-integer quantized when certain assumptions about the self-energy are made. Away from these conditions, which cannot or are not usually considered within the formalism of nH Hamiltonians, we find that such a quantization is usually lost and the Chern number vanishes, and that in special cases, it can change to integer quantization.
Predicting the evolution of photospheric magnetic field in solar active regions using deep learning
Authors: Bai Liang , Bi Yi , Yang Bo , Hong Jun-Chao , Xu Zhe , Shang Zhen-Hong , Liu Hui , Ji Hai-Sheng , Ji Kai-Fan 
Published: June 2021

Abstract
The continuous observation of the magnetic field by the Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) produces numerous image sequences in time and space. These sequences provide data support for predicting the evolution of photospheric magnetic field. Based on the spatiotemporal long short-term memory (LSTM) network, we use the preprocessed data of photospheric magnetic field in active regions to build a prediction model for magnetic field evolution. Because of the elaborate learning and memory mechanism, the trained model can characterize the inherent relationships contained in spatiotemporal features. The testing results of the prediction model indicate that (1) the prediction pattern learned by the model can be applied to predict the evolution of new magnetic field in the next 6 hours that have not been trained, and predicted results are roughly consistent with real observed magnetic field evolution in terms of large-scale structure and movement speed; (2) the performance of the model is related to the prediction time; the shorter the prediction time, the higher the accuracy of the predicted results; (3) the performance of the model is stable not only for active regions in the north and south but also for data in positive and negative regions. Detailed experimental results and discussions on magnetic flux emergence and magnetic neutral lines finally show that the proposed model could effectively predict the large-scale and short-term evolution of the photospheric magnetic field in active regions. Moreover, our study may provide a reference for the spatiotemporal prediction of other solar activities.
Search for variable stars in the open cluster NGC 1245
Authors: Shen Dong-Xiang , Zhang Yu , Li Chun-Yan , Zeng Xiang-Yun , Lü Guo-Liang , Zhu Chun-Hua , Hu Qing-Shun , Liu Jin-Zhong 
Published: June 2021

Abstract
We used the Nanshan 1m telescope of Xinjiang Astronomical Observatory to investigate variable stars in the region of NGC 1245. We detected 55 variable stars in the field of view, 42 of them are newly discovered: six binaries, 22 pulsating stars, 14 unclassified variables. We discussed the physical properties and classifications of the variable stars through their light curves. The cross-matching with the members catalog of NGC 1245 suggests that 10 variable stars are identified as probable cluster members, which including one EW type binary, eight δ Scuti stars, and an unknown type variable star. Since the difference of the members catalog of the cluster and the limitations of the instrument, there are some huge differences in the variable list of NGC 1245 compared with the previous work.
Magneto-acoustic waves in magnetic twisted flux tubes
Authors: Wu Wei , Sych Robert , Chen Jie , Su Jiang-Tao 
Published: June 2021

Abstract
At present, many works about MHD wave diagnostics in magnetic flux tubes are based on some pioneering works not considering the contributions of magnetic twist. Other works considered the effect on MHD waves, but the dispersion relationship they presented only gave the wave modes of m = 0,1,2… The kink mode of m = –1 was absent. Therefore, in this work we present a complete dispersion relationship that includes both magnetic twist and the wave mode of m = –1. Analogous to the m = +1 wave mode, the mode of m = –1 also exhibits the mode change at finite kr
0, from body to surface mode. The phase speeds of this mode are usually less than those of m = +1 mode. The harmonic curves of m = ± 1 modes in dispersion relationship diagrams are approximately symmetric in respect to a characteristic velocity, e.g. the tube velocity in flux tubes. Based on the present dispersion relationship, we revisit the issue of spiral wave patterns in sunspots and find that the magnetic twist has no great influence on their morphology in the frame of linear perturbation analysis.
Heavy element contributions of rotating massive stars to interstellar medium
Authors: Wu Rui-Qing , Zhu Chun-Hua , Lü Guo-Liang , Wang Zhao-Jun , Liu He-Lei 
Published: June 2021

AbstractEmploying the stellar evolution code Modules for Experiments in Stellar Astrophysics (MESA), we calculate yields of heavy elements from massive stars via stellar wind and core−collapse supernova (CCSN) ejecta to the interstellar medium (ISM). In our models, the initial masses (Mini) of massive stars are taken from 13 to 80M⊙, their initial rotational velocities (V) are 0, 300 and 500 km s−1, and their metallicities are [Fe/H] = –3, –2, –1 and 0. The yields of heavy elements coming from stellar winds are mainly affected by stellar rotation which changes the chemical abundances of stellar surfaces via chemically homogeneous evolution, and enhances mass-loss rate. We estimate that the stellar wind can produce heavy element yields of about 10−2(for low metallicity models) to a mass of severalM⊙(for low metallicity and rapid rotation models). The yields of heavy elements produced by CCSN ejecta also depend on the large amount of remnant mass which is mainly determined by the mass of the CO-core. Our models calculate that the yields of heavy elements produced by CCSN ejecta can get up to severalM⊙. Compared with stellar wind, CCSN ejecta has a greater contribution to the heavy elements in ISM. We also compare the56Ni yields calculated in this work with the observational estimate. Our models only explain the56Ni masses produced by faint SNe or normal SNe with progenitor mass lower than about 25M⊙, and greatly underestimate the56Ni masses produced by stars with masses higher than about 30M⊙.
Extreme space weather events caused by super active regions during solar cycles 21-24
Authors: Le Gui-Ming , Liu Gui-Ang , Zhao Ming-Xian , Mao Tian , Xu Ping-Guo 
Published: June 2021

Abstract
Extreme space weather events including ≥X5.0 flares, ground level enhancement (GLE) events and super geomagnetic storms (Dst ≤ –250 nT) caused by super active regions (SARs) during solar cycles 21–24 were studied. The total number of ≥X5.0 solar flares was 62, among which 41 were X5.0-X9.9 flares and 21 were ≥X10.0 flares. We found that 83.9% of the ≥X5.0 flares were produced by SARs; 78.05% of the X5.0-X9.9 and 95.24% of the ≥X10.0 solar flares were produced by SARs; 46 GLEs were registered during solar cycles 21–24, and 25 GLEs were caused by SARs, indicating that 54.3% of the GLEs were caused by SARs; 24 super geomagnetic storms were recorded during solar cycles 21–24, and 12 of them were caused by SARs, namely 50% of the super geomagnetic storms were caused by SARs. We ascertained that only 29 SARs produced ≥X5.0 flares, 15 SARs generated GLEs and 10 SARs triggered super geomagnetic storms. Of the 51 SARs, only 33 SARs produced at least one extreme space weather event, while none of the other 18 SARs could trigger an extreme space weather event. There were only four SARs and each of them generated not only a ≥X5.0 flare, but also a GLE event and a super geomagnetic storm. Most of the extreme space weather events caused by the SARs appeared during solar cycles 22 and 23, especially for GLE events and super geomagnetic storms. The longitudinal distributions of source locations for the extreme space weather events caused by SARs were also studied.
The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity
Authors: Misaki Masaya , Bodurka Jerzy 
Published: June 2021

Abstract

Objective. Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity. Approach. We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT). Main results. All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time. Significance. The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.
Compactible Level Measurement and Forewarning in Petrol Station
Authors: Moorthy V Prasanna , Subramanian Suntrakanesh , Balaji O S P 
Published: June 2021

Abstract
It is known that India is the third-largest country in fossil fuel consumption, and the economy of India dramatically depends on the transportation of goods and services which require fuel. Petrol stations should ensure the correct fuel stock and restore the fuel on time once it gets exhausted. Thus, the customer’s needs get satisfied. Generally, the level measurement in fuel storage tanks is carried out by physical methods which involve dipsticks or using some level sensors, float switch, and load cell. Dipsticks are more often used in level measurement. The usage of dipsticks involves a human operator removing and read the dipstick scale. The measured data is communicated to the refill manager through phone calls, and the petrol station gets refilled. The refilling of petrol station may get late due to improper communication and late transportation of fuel vehicles when stuck in traffic. This work aims to overcome the material problem and improve efficiency and accuracy in the level measurement of fuel by introducing automation in measurement and communication. The depth of the fuel tank is estimated by a level sensor accompanied by a Wi-fi module to bring out automation. The measured data can be accessed directly by the user through a telegram app. The level of the fuel tank is automatically indicated. The dynamically changing data of the fuel tank can also be acquired by using the telegram app. The refill manager will be aware of the fuel level, and fuel can be restored on time. This product will be very useful in remote areas. This product has vast potential and will play a significant role in fuel measurement for key oil companies.
AIOT based Real Time Environment Monitoring and Tracking System for Soldiers using SN-MQTT Protocol
Authors: Manikandan T , Ranjani J , V Ramnya , Preethi S 
Published: June 2021

Abstract
In the crisis filled world, it is necessary to protect our Country and its resources. Where a strong military force will enable in safeguarding the nation security, wealth and our valuable lives too. Soldiers are not only involved in providing protection against the military attacks but also plays a crucial role in resolving problems thereby maintaining prevalence of peace in the country. Therefore, it is necessary to safeguard and protect soldiers from dangers as they are the guardians to enrich the prosperity of one’s country. The proposed project focuses on monitoring the soldier’s environmental attacks like detection of gun firing attacks, toxic gas detection, and emergency alert in case of threats. Followed by creating a virtual fence using Global Positioning System (GPS) to track and guard them within the safer zone and with inclusion of two-way wireless communication between the soldier & the surveillance unit using Wi-Fi Module in case of emergency return from the battle field thereby protecting them from the possibilities of danger. The technologies in particular Artificial Intelligence of Things i.e. the collaboration of Internet of Things (IoT) with deep learning (RNN-LSTM Model) helps in achieving information transfer between soldier and administrator followed by prediction of toxic gas presence in the atmosphere. The Administrator at the surveillance unit will monitor the real time status of the soldier in the webpage created using Node Red Programming Tool where the data are carried out by means of Sensor Networks - Message Queuing Telemetry Transport Protocol (MQTT). Thus this system ensures the security of the soldier.
IoT based Touch-free Attendance System (ITAS)
Authors: Tamilselvan S , Ramesh R , Niveda R , Poonguzhali P , Dharani S 
Published: June 2021

Abstract
In this paper, we are proposing a contactless temperature measuring and attendance monitoring system because of the pandemic COVID-19. This design is developed using Node MCU, MLX90614 Infrared Thermometer, RFID Reader, and Ultrasonic Sensor. It measures body temperature with an infrared temperature sensor without any physical contact. An RFID card reader is used for scanning the identity card. The ID number and the temperature of students are uploaded into the webpage. This webpage can be monitored over the internet. The details such as Entry time, ID number, No. of students and temperature measurements are stored in the server and displayed in webpage. An Ultrasonic Sensor is used to measure the distance between the sensor and the person. The temperature sensor can measure the temperature when the distance between the sensor and person is around 10 cm for better accuracy. This system is essential in this pandemic situation to avoid the infections.
Low Power and Efficient Floating-Point Architecture for Division and Square Root
Authors: Hema Priya K , Arulmurugan L , Ramesh R , Nithya B 
Published: June 2021

Abstract
The modern processor requires multiple dedicates, fully pipelined architecture uses instructional level parallelism. So, this pipeline is very important in it. The proposed system is implemented using the fully pipelined architecture FPU in FPGA’s. Square root and division have many complications in DSP but it is important to implement in FPGA because it is so tough. Some of them will not implement the add and multiply components when compared to the slow or large which will match the real time applications. In every clock cycle, pipeline should have the capability of accepting the square root and divider. In single precision floating point Verilog code is written and implemented using spartan 6 FPGA for sqrt/division. The operation latency and issue rate have 18 clock cycles and 15 clock cycles. It uses many adders/subtractors in other high pipelined implementations. The operation latency and issue rate have 15 clock cycles and 1 clock cycles. It reduces power dissipation. It has some advantages like area utilization will be less and latency bound.