bluuebunny/crossref_metadata_2025_split huggingface dataset:
A transition from enemies to allies: how viruses improve drought resilience in plants
Authors: Prakash Ved , Sharma Veerendra , Devendran Ragunathan , Prajapati Ramgopal , Ahmad Bilal , Kumar Ritesh 
Published: July 2024

AbstractGlobal crop production is severely affected by environmental factors such as drought, salinity, cold, flood etc. Among these stresses, drought is one of the major abiotic stresses reducing crop productivity. It is expected that drought conditions will further increase because of the increasing global temperature. In general, viruses are seen as a pathogen affecting the crop productivity. However, several researches are showing that viruses can induce drought tolerance in plants. This review explores the mechanisms underlying the interplay between viral infections and the drought response mechanisms in plants. We tried to address the molecular pathways and physiological changes induced by viruses that confer drought tolerance, including alterations in hormone signaling, antioxidant defenses, scavenging the reactive oxygen species, role of RNA silencing and miRNA pathway, change in the expression of several genes including heat shock proteins, cellulose synthase etc. Furthermore, we discuss various viruses implicated in providing drought tolerance and examine the range of plant species exhibiting this phenomenon. By applying current knowledge and identifying gaps in understanding, this review aims to provide valuable insights into the complex dynamics of virus-induced drought tolerance in plants, paving the way for future research directions and practical applications in sustainable agriculture.
MyBioethics: How Ed-Tech Enables Discovery-Driven Empirical Bioethics Research
Authors: Janhonen Joel , Värttö Mikko , Saxén Heikki 
Published: July 2024

AbstractDigital tools have granted new opportunities to engage people with bioethical discussion and rehearsed decision-making. The ongoing development of the MyBioethics mobile application links these together within a digital space designed to encourage deliberation and research participation by inviting users into the process of discovery. Besides educational purposes, this has enabled a unique way to gather real-world observations. A research procedure was designed to harness the functionality of a mobile application. Quantitative data was generated by dilemma scenarios and integrated surveys that measure and inform users about their psychological and epistemic tendencies. The resulting analysis enabled the possible influence of these factors on moral judgment formation to be investigated—leading to the preliminary identification of prospective relationships. The adopted methodology is crowdsourced and explorative. We seek to generate hypotheses as well as facilitate ethical reflection among users. This work is a proof-of-concept. The main finding is the tentative confirmation of the approach. A digital teaching tool can function to advance empirical bioethics research. The gathered data unveiled prospective areas of academic interest and yielded observations that may contain valuable reflective insights for individual end users. Digital bioethics brings along new opportunities to engage a diverse user base in a way that provides educational resources, challenges ethical preconceptions and intuitions, allows inclusion in research efforts, and encourages autonomous decision-making. Ed-tech applications appear suitable for investigating personal tendencies that are influencing our moral judgments. Digital environments could be designed to surface unarticulated factors behind our held positions and challenge unquestioned moral notions.
Utilization of artificial intelligence and machine learning in chemistry education: a critical review
Authors: Iyamuremye Aloys , Niyonzima Francois Niyongabo , Mukiza Janvier , Twagilimana Innocent , Nyirahabimana Pascasie , Nsengimana Theophile , Habiyaremye Jean Dieu , Habimana Olivier , Nsabayezu Ezechiel 
Published: July 2024

AbstractThe current study aimed to criticize the existing literature on the utilization of artificial intelligence (AI) and machine learning (ML) in teaching and learning chemistry. A comprehensive critical literature review was conducted using electronic databases such as Scopus, PubMed, ISI, Google Scholar, ERIC, Web of Science, and JSTOR. In this regard, 62 articles were extracted from these electronic databases. During the selection of the literature inclusion and exclusion criteria were applied. The inclusion criteria include empirical and theoretical studies examining the effectiveness, challenges, and opportunities of AI/ML, and articles from 2018 to 2024 and written in English. On the other side, the exclusion criteria include literature that unrelated to education, lacking empirical evidence, or not peer-reviewed, as well as non-English publications, and published before 2018. This was done to gain insights into the current implementation status of AI and ML as well as critical issues of using these approaches in chemistry education. The study employed a critical review of the literature, which involves a critical analysis of the themes and concepts that emerge from the selected literature and identifies the opportunities and challenges surrounding the utilization of these technologies. The results revealed that there are opportunities for the integration of AI and ML in chemistry education, including personalized learning experiences, teacher assistance, and accessibility to learning materials. In this regard, intelligent tutoring systems and adaptive learning platforms were identified as potential aides for teachers in various aspects of teaching. The study also revealed the limitations and challenges surrounding AI and ML, such as the dependence on preexisting data, potential biases in models, and concerns around data privacy and security. Moreover, the findings also indicated that the implementation of AI and ML in chemistry education is still in its juvenile stage. Thus, teacher training programs are needed to equip teachers with the necessary skills for the use of these technologies effectively in the classroom. In addition, more efforts should be made to facilitate research, collaboration, and the development of policies and regulations that ensure responsible use of these technologies in the teaching and learning process.
The secret history of Germiyan, or a reassessment of the debates on the origins of the Germiyanids
Authors: Thurin Romain 
Published: July 2024

AbstractIn the early fourteenth century, decades before the Ottomans became the sole rulers of Anatolia, the Germiyanid beylik stood as a dominant force among the principalities that had emerged in western Anatolia following the demise of the Seljuks (1307). Nonetheless, to date, the exact origins and ethno-cultural background of the Germiyanids remain unclear. This article re-evaluates previous theories and posits that the embryo of what eventually became the Germiyanids formed between the eleventh and thirteenth centuries in the lowlands between Malatya and Lake Van, and that the name “Germiyan” was of Kurdish origin. It also suggests that an intense proto-Yedizi proselytism took place in eastern Anatolia before the Germiyanids migrated to western Anatolia. Beyond its significance to the history of the Seljuks, the Ottomans, the Mongols, and Byzantium, this paper challenges the prevailing narrative that views the emergence of the beyliks as an exclusively Turkic phenomenon and sheds light on the role played by non-Turkic people, including Kurds and Arabs, in their formation.
The Venus Global Ionosphere‐Thermosphere Model (V‐GITM): A Coupled Thermosphere and Ionosphere Formulation
Authors: Ponder Brandon M. , Ridley Aaron J. , Bougher Stephen W. , Pawlowski David , Brecht Amanda 
Published: July 2024

AbstractThis paper introduces the new Venus global ionosphere‐thermosphere model (V‐GITM) which incorporates the terrestrial GITM framework with Venus‐specific parameters, ion‐neutral chemistry, and radiative processes in order to simulate some of the observable features regarding the temperatures, composition, and dynamical structure of the Venus atmosphere from 70 to 170 km. Atmospheric processes are included based upon formulations used in previous Venus GCMs, several augmentations exist, such as improved horizontal and vertical momentum equations and tracking exothermic chemistry. Explicitly solving the momentum equations allows for the exploration of its dynamical effects on the day‐night structure. In addition, V‐GITM's use of exothermic chemistry instead of a strong heating efficiency accounts for the heating due to the solar EUV while producing comparable temperatures to empirical models. V‐GITM neutral temperatures and neutral‐ion densities are compared to upper atmosphere measurements obtained from Pioneer Venus and Venus Express. V‐GITM demonstrates asymmetric horizontal wind velocities through the cloud tops to the middle thermosphere and explains the mechanisms for sustaining the wind structure. In addition, V‐GITM produces reasonable dayside ion densities and shows that the neutral winds can carry the ions to the nightside via an experiment advecting .
The Capability of Amphibole in Tracing the Physicochemical Processes of Magma Mixing
Authors: Li Mingjian , Zeng Yunchuan , Tiepolo Massimo , Xu Jifeng , Cannaò Enrico , Forni Francesca , Huang Feng 
Published: July 2024

AbstractThis study explores the capability of amphibole in tracing the physicochemical process of magma mixing through spatially associated gabbros, mafic microgranular enclaves (MMEs) and granodiorites from central Tibet. These rocks share similar zircon ages as well as zircon Hf‐O and plagioclase Sr isotopes. However, the amphiboles within the gabbros and granodiorites have different Sr and B isotope compositions, while amphiboles with both heterogeneous isotopic imprints occur in the MMEs. According to data and modeling, significant mixing of two isotopically distinct magmas is recorded by amphibole but not by zircon and plagioclase. Based on a synthesis of petrography, geochemistry and thermobarometry, we interpret this inconsistency by the crystallization order of minerals and propose that magma mixing occurred after the parent magma was emplaced at ∼10 km and cooled to ∼750°C. Our study highlights that amphibole may be a more sensitive tracer of magma mixing relative to other commonly used methods.
Increases of Offshore Wind Potential in a Warming World
Authors: Shen Cheng , Li Zhi‐Bo , Yuan Hui‐Shuang , Yu Yue , Lei Yadong , Chen Deliang 
Published: July 2024

AbstractOffshore wind farms, a rapidly expanding sector within wind energy, are playing a significant role in achieving global carbon neutrality, and this trend is to continue. Here, we utilize ERA5 reanalysis to correct offshore wind speed trends predicted by CMIP6 models. This approach led to enhanced projections for changes in offshore Wind Power Density (WPD) under four Shared Socioeconomic Pathways (SSPs) scenarios. Throughout the 21st century, global offshore WPD is projected to follow an upward trend across all SSP scenarios. Notably, Europe stands out with the most substantial increase in offshore WPD among regions with higher current installations, projected to reach up to 26% under 4°C global warming. Our study uncovers a notable increase of global offshore WPD in a warmer climate, which offers valuable insights for the strategic planning of future global wind energy.
Improved Energy Resolution Measurements of Electron Precipitation Observed During an IPDP‐Type EMIC Event
Authors: Clilverd M. A. , Rodger C. J. , Hendry A. T. , Lozinski A. R. , Sauvaud J.‐A. , Lessard M. R. , Raita T. 
Published: July 2024

AbstractHigh energy resolution DEMETER satellite observations from the Instrument for the Detection of Particle (IDP) are analyzed during an electromagnetic ion cyclotron (EMIC)‐induced electron precipitation event. Analysis of an Interval Pulsation with Diminishing Periods (IPDP)‐type EMIC wave event, using combined satellite observations to correct for incident proton contamination, detected an energy precipitation spectrum ranging from ∼150 keV to ∼1.5 MeV. While inconsistent with many theoretical predictions of >1 MeV EMIC‐induced electron precipitation, the finding is consistent with an increasing number of experimentally observed events detected using lower resolution integral channel measurements on the POES, FIREBIRD, and ELFIN satellites. Revised and improved DEMETER differential energy fluxes, after correction for incident proton contamination shows that they agree to within 40% in peak flux magnitude, and 85 keV (within 40%) for the energy at which the peak occurred as calculated from POES integral channel electron precipitation measurements. This work shows that a subset of EMIC waves found close to the plasmapause, that is, IPDP‐type rising tone events, can produce electron precipitation with peak energies substantially below 1 MeV. The rising tone features of IPDP EMIC waves, along with the association with the high cold plasma density regime, and the rapidly varying electron density gradients of the plasmapause may be an important factor in the generation of such low energy precipitation, co‐incident with a high energy tail. Our work highlights the importance of undertaking proton contamination correction when using the high‐resolution DEMETER particle measurements to investigate EMIC‐driven electron precipitation.
A decomposition of light’s spin angular momentum density
Authors: Vernon Alex J. , Golat Sebastian , Rigouzzo Claire , Lim Eugene A. , Rodríguez-Fortuño Francisco J. 
Published: July 2024

AbstractLight carries intrinsic spin angular momentum (SAM) when the electric or magnetic field vector rotates over time. A familiar vector equation calculates the direction of light’s SAM density using the right-hand rule with reference to the electric and magnetic polarisation ellipses. Using Maxwell’s equations, this vector equation can be decomposed into a sum of two distinct terms, akin to the well-known Poynting vector decomposition into orbital and spin currents. We present the first general study of this spin decomposition, showing that the two terms, which we call canonical and Poynting spin, are chiral analogies to the canonical and spin momenta of light in its interaction with matter. Like canonical momentum, canonical spin is directly measurable. Both canonical and Poynting spin incorporate spatial variation of the electric and magnetic fields and are influenced by optical vortices. The decomposition allows us to show that a linearly polarised vortex beam, which has no total SAM, can nevertheless exert longitudinal chiral pressure due to equal and opposite canonical and Poynting spins.
Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits
Authors: Gao Sheng , Chen Hang , Wang Yichen , Duan Zhengyang , Zhang Haiou , Sun Zhi , Shen Yuan , Lin Xing 
Published: July 2024

AbstractWireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.
Protein Condensate Atlas from predictive models of heteromolecular condensate composition
Authors: Saar Kadi L. , Scrutton Rob M. , Bloznelyte Kotryna , Morgunov Alexey S. , Good Lydia L. , Lee Alpha A. , Teichmann Sarah A. , Knowles Tuomas P. J. 
Published: July 2024

AbstractBiomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.
Toddlers strategically adapt their information search
Authors: Poli Francesco , Li Yi-Lin , Naidu Pravallika , Mars Rogier B. , Hunnius Sabine , Ruggeri Azzurra 
Published: July 2024

AbstractAdaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers’ search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults’.
Realization of higher-order topological lattices on a quantum computer
Authors: Koh Jin Ming , Tai Tommy , Lee Ching Hua 
Published: July 2024

AbstractProgrammable quantum simulators may one day outperform classical computers at certain tasks. But at present, the range of viable applications with noisy intermediate-scale quantum (NISQ) devices remains limited by gate errors and the number of high-quality qubits. Here, we develop an approach that places digital NISQ hardware as a versatile platform for simulating multi-dimensional condensed matter systems. Our method encodes a high-dimensional lattice in terms of many-body interactions on a reduced-dimension model, thereby taking full advantage of the exponentially large Hilbert space of the host quantum system. With circuit optimization and error mitigation techniques, we measured on IBM superconducting quantum processors the topological state dynamics and protected mid-gap spectra of higher-order topological lattices, in up to four dimensions, with high accuracy. Our projected resource requirements scale favorably with system size and lattice dimensionality compared to classical computation, suggesting a possible route to useful quantum advantage in the longer term.
Single cell transcriptomes and multiscale networks from persons with and without Alzheimer’s disease
Authors: Wang Qi , Antone Jerry , Alsop Eric , Reiman Rebecca , Funk Cory , Bendl Jaroslav , Dudley Joel T. , Liang Winnie S. , Karr Timothy L. , Roussos Panos , Bennett David A. , De Jager Philip L. , Serrano Geidy E. , Beach Thomas G. , Van Keuren-Jensen Kendall , Mastroeni Diego , Reiman Eric M. , Readhead Benjamin P. 
Published: July 2024

AbstractThe emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer’s disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.
Correlative single-molecule and structured illumination microscopy of fast dynamics at the plasma membrane
Authors: Winkelmann Hauke , Richter Christian P. , Eising Jasper , Piehler Jacob , Kurre Rainer 
Published: July 2024

AbstractTotal internal reflection fluorescence (TIRF) microscopy offers powerful means to uncover the functional organization of proteins in the plasma membrane with very high spatial and temporal resolution. Traditional TIRF illumination, however, shows a Gaussian intensity profile, which is typically deteriorated by overlaying interference fringes hampering precise quantification of intensities—an important requisite for quantitative analyses in single-molecule localization microscopy (SMLM). Here, we combine flat-field illumination by using a standard πShaper with multi-angular TIR illumination by incorporating a spatial light modulator compatible with fast super-resolution structured illumination microscopy (SIM). This distinct combination enables quantitative multi-color SMLM with a highly homogenous illumination. By using a dual camera setup with optimized image splitting optics, we achieve a versatile combination of SMLM and SIM with up to three channels. We deploy this setup for establishing robust detection of receptor stoichiometries based on single-molecule intensity analysis and single-molecule Förster resonance energy transfer (smFRET). Homogeneous illumination furthermore enables long-term tracking and localization microscopy (TALM) of cell surface receptors identifying spatial heterogeneity of mobility and accessibility in the plasma membrane. By combination of TALM and SIM, spatially and molecularly heterogenous diffusion properties can be correlated with nanoscale cytoskeletal organization and dynamics.