Multiple Research Fields Research Articles

ThermoBase: A database of the phylogeny and physiology of thermophilic and hyperthermophilic organisms.

Thermophiles and hyperthermophiles are those organisms which grow at high temperature (> 40°C). The unusual properties of these organisms have received interest in multiple fields of biological research, and have found applications in biotechnology, especially in industrial processes. However, there are few listings of thermophilic and hyperthermophilic organisms and their relevant environmental and physiological data. Such repositories can be used to standardize definitions of thermophile and hyperthermophile limits and tolerances and would mitigate the need for extracting organism data from diverse literature sources across multiple, sometimes loosely related, research fields. Therefore, we have developed ThermoBase, a web-based and freely available database which currently houses comprehensive descriptions for 1238 thermophilic or hyperthermophilic organisms. ThermoBase reports taxonomic, metabolic, environmental, experimental, and physiological information in addition to literature resources. This includes parameters such as coupling ions for chemiosmosis, optimal pH and range, optimal temperature and range, optimal pressure, and optimal salinity. The database interface allows for search features and sorting of parameters. As such, it is the goal of ThermoBase to facilitate and expedite hypothesis generation, literature research, and understanding relating to thermophiles and hyperthermophiles within the scientific community in an accessible and centralized repository. ThermoBase is freely available online at the Astrobiology Habitable Environments Database (AHED; https://ahed.nasa.gov), at the Database Center for Life Science (TogoDB; http://togodb.org/db/thermobase), and in the S1 File.

Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy.

Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.

What is the age of flowering plants?

The origin of flowering plants (angiosperms) was one of the most transformative events in the history of our planet. Despite considerable interest from multiple research fields, numerous questions remain, including the age of the group as a whole. Recent studies have reported a perplexing range of estimates for the crown-group age of angiosperms, from ~140 million years (Ma; Early Cretaceous) to 270 Ma (Permian). Both ends of the spectrum are now supported by both macroevolutionary analyses of the fossil record and fossil-calibrated molecular dating analyses. Here, we first clarify and distinguish among the three ages of angiosperms: the age of their divergence with acrogymnosperms (stem age); the age(s) of emergence of their unique, distinctive features including flowers (morphological age); and the age of the most recent common ancestor of all their living species (crown age). We then demonstrate, based on recent studies, that fossil-calibrated molecular dating estimates of the crown-group age of angiosperms have little to do with either the amount of molecular data or the number of internal fossil calibrations included. Instead, we argue that this age is almost entirely conditioned by its own prior distribution (typically a calibration density set by the user in Bayesian analyses). Lastly, we discuss which future discoveries or novel types of analyses are most likely to bring more definitive answers. In the meantime, we propose that the age of angiosperms is best described as largely unknown (140-270 Ma) and that contrasting age estimates in the literature mostly reflect conflicting prior distributions. We also suggest that future work that depends on the time scale of flowering plant diversification be designed to integrate over this vexing uncertainty.

Citizens as an innovation source in sustainability transitions – linking the directionality of innovations with the locus of the problem in transformative innovation policy

This article argues that citizens play a key role in sustainability transitions: Citizens have unique knowledge on why social problems occur and experiment with possible solutions to these problems. Yet transformative innovation policy – a policy frame that promotes socio-technical systems change – is guided by a producer-centric innovation paradigm, which focuses on technological breakthroughs rather than social changes driven by citizens. By drawing on multiple research fields, and by using the example of household food waste, this article challenges this paradigm and asserts that addressing sustainability challenges requires a policy frame that defines citizens as an innovation source.

Unifying approaches to Functional Marine Connectivity for improved marine resource management: the European SEA-UNICORN COST Action

Truly sustainable development in a human-altered, fragmented marine environment subject to unprecedented climate change, demands informed planning strategies in order to be successful. Beyond a simple understanding of the distribution of marine species, data describing how variations in spatio-temporal dynamics impact ecosystem functioning and the evolution of species are required. Marine Functional Connectivity (MFC) characterizes the flows of matter, genes and energy produced by organism movements and migrations across the seascape. As such, MFC determines the ecological and evolutionary interdependency of populations, and ultimately the fate of species and ecosystems. Gathering effective MFC knowledge can therefore improve predictions of the impacts of environmental change and help to refine management and conservation strategies for the seas and oceans. Gathering these data are challenging however, as access to, and survey of marine ecosystems still presents significant challenge. Over 50 European institutions currently investigate aspects of MFC using complementary methods across multiple research fields, to understand the ecology and evolution of marine species. The aim of SEA-UNICORN, a COST Action within the European Union Horizon 2020 framework programme, is to bring together this research effort, unite the multiple approaches to MFC, and to integrate these under a common conceptual and analytical framework. The consortium brings together a diverse group of scientists to collate existing MFC data, to identify knowledge gaps, to enhance complementarity among disciplines, and to devise common approaches to MFC. SEA-UNICORN will promote co-working between connectivity practitioners and ecosystem modelers to facilitate the incorporation of MFC data into the predictive models used to identify marine conservation priorities. Ultimately, SEA-UNICORN will forge strong forward-working links between scientists, policy-makers and stakeholders to facilitate the integration of MFC knowledge into decision support tools for marine management and environmental policies.

Application of the Improved Multipopulation Genetic Algorithm in the TMD Controlled System considering Soil‐Structure Interaction

With the advent of globalization, computing speed has increased tremendously, greatly advancing algorithm research in multiple fields. This paper studies the parameter optimization problem of the improved multipopulation genetic algorithm in the tuned mass damper (TMD) structure considering the soil‐structure interaction (SSI) effect. The Newmark time‐domain analysis method was used to analyze the dynamic response of a 40‐story building under the excitation of EL Centro waves and Tangshan waves in China, respectively. The mass, damping coefficient, and spring stiffness of TMD system are used as the design variables of the controller. To reduce structural damage and obtain better comfort, the displacement response and acceleration response are optimized simultaneously in this paper, achieving multiobjective optimization. The results show that the improved multipopulation genetic algorithm method has faster convergence speed and greater accuracy than the traditional genetic algorithm; thus it can be applied to the TMDs parameter optimization of high‐rise buildings. Besides, the soil types have a great influence on TMD parameter optimization and structural time history response. If ignoring SSI effect will lead to underestimation of parameter design, the reason is that the soft soil foundations can absorb a lot of seismic energy compared with rigid foundations and then reduce the effect of seismic excitation on the structure. The intention of the research helps researchers to better understand vibration control and provides suggestions for the application of TMD in high‐rise buildings.

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping.

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

The developmental origins of heterodonty and acrodonty as revealed by reptile dentitions.

Despite the exceptional diversity and central role of dentitions in vertebrate evolution, many aspects of tooth characters remain unknown. Here, we exploit the large array of dental phenotypes in acrodontan lizards, including EDA mutants showing the first vertebrate example of positional transformation in tooth identity, to assess the developmental origins and evolutionary patterning of tooth types and heterodonty. We reveal that pleurodont versus acrodont dentition can be determined by a simple mechanism, where modulation of tooth size through EDA signaling has major consequences on dental formula, thereby providing a new flexible tooth patterning model. Furthermore, such implication of morphoregulation in tooth evolution allows predicting the dental patterns characterizing extant and fossil lepidosaurian taxa at large scale. Together, the origins and diversification of tooth types, long a focus of multiple research fields, can now be approached through evo-devo approaches, highlighting the importance of underexplored dental features for illuminating major evolutionary patterns.

RADIOCARBON AND ATMOSPHERIC14CO2PIONEER ATHOL RAFTER

ABSTRACTDirect atmospheric14CO2measurements began in New Zealand in 1954, initially to improve14C as a dating tool, but quickly evolving into a method for understanding the carbon cycle. These early14CO2measurements immediately demonstrated the existence of an “Atom Bomb Effect,” as well as an “Industrial Effect.” These two gigantic tracer experiments have been utilized via14CO2measurements over the years to produce a wealth of knowledge in multiple research fields including atmospheric carbon cycle research, oceanography, soil science, and aging of post-bomb materials.

Electron crystallography of chiral and non-chiral small molecules

Electron crystallography has recently gained attentions in multiple fields of research, as it has been demonstrated to determine atomic structures for inorganic, organic, and macromolecular materials from nano-sized crystals that were not amenable to conventional X-ray crystallography. Here, we demonstrate continuous-rotation microcrystal electron diffraction (microED) in a 200 kV transmission electron microscope using a DE-64 camera–a low-noise direct electron detector that can accommodate a linear response up to ∼1200 electrons per pixel per second at 20 fps with 2x-hardware-binning, making it ideal for acquisition of high-quality diffraction patterns. We have used this method and camera to determine a 0.75 Å structure of an organic molecule, biotin, with an exceptional goodness-of-fit, as well as a 0.88 Å structure of a chiral molecule, L-serine.

The Role of Private Environmental Governance in Climate Adaptation

This Article examines the role of private environmental governance (PEG) in climate change adaptation. PEG occurs when private organizations perform traditionally governmental functions such as providing public goods and reducing negative externalities. PEG initiatives that target climate change mitigation have expanded rapidly in the last decade and have been the subject of research in multiple fields, but PEG initiatives that target climate change adaptation have received less attention. As a first step, the Article develops a definition of private governance regarding climate adaption, identifies several types of PEG adaptation initiatives, and briefly identifies research gaps.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Inflammation, a common mechanism in frailty and COVID-19, and stem cells as a therapeutic approach.

As our life expectancy increases, specific medical conditions appear, and new challenges are met in terms of global health. Frailty has become a medical and scientific concept to define pathologies where inflammation, depressed immune system, cellular senescence, and molecular aging converge. But more importantly, frailty is the ultimate cause of death that limits our life span and deteriorates health in an increasing proportion of the world population. The difficulty of tackling this problem is the combination of factors that influence frailty appearance, such as stem cells exhaustion, inflammation, loss of regeneration capability, and impaired immunomodulation. To date, multiple research fields have found mechanisms participating in this health condition, but to make progress, science will need to investigate frailty with an interdisciplinary approach. This article summarizes the current efforts to understand frailty from their processes mediated by inflammation, aging, and stem cells to provide a new perspective that unifies the efforts in producing advanced therapies against medical conditions in the context of frailty. We believe this approach against frailty is particularly relevant to COVID‐19, since people in a state of frailty die more frequently due to the hyperinflammatory process associated with this infection.

Enhanced Detection of Major Pathogens and Toxins in Poultry and Livestock With Zoonotic Risks Using Nanomaterials-Based Diagnostics.

Nanotechnology has gained prominence over the recent years in multiple research and application fields, including infectious diseases in healthcare, agriculture, and veterinary science. It remains an attractive and viable option for preventing, diagnosing, and treating diseases in animals and humans. The apparent efficiency of nanomaterials is due to their unique physicochemical properties and biocompatibility. With the persistence of pathogens and toxins in the poultry and livestock industries, rapid diagnostic tools are of utmost importance. Though there are many promising nanomaterials-based diagnostic tests specific to animal disease-causing agents, many have not achieved balanced sensitivity, specificity, reproducibility, and cost-effectiveness. This mini-review explores several types of nanomaterials, which provided enhancement on the sensitivity and specificity of recently reported diagnostic tools related to animal diseases. Recommendations are also provided to facilitate more targeted animal populations into the development of future diagnostic tools specifically for emerging and re-emerging animal diseases posing zoonotic risks.

Propagating Chiral Phonons in Three-Dimensional Materials

Chiral phonons were initially proposed and experimentally verified in two-dimensional (2D) systems. Their intriguing effects have generated profound impacts on multiple research fields. However, all chiral phonons reported to date are constrained to be local, in the sense that their group velocities vanish identically. Here, we propose the concept of propagating 3D chiral phonons, which can transport the information on chirality and angular momentum. Guided by the necessary conditions and using first-principles calculations, we demonstrate their existence in WN2. The chirality, group velocity, and pseudoangular momentum are analyzed. Based on their selective coupling with valley electrons and photons, we propose an experimental setup to detect the unique feature of propagating chiral phonons. Our work endows chiral phonons with a crucial character-the ability to propagate and transport quantized information, which creates a new research direction and opens up the possibility to design novel phononic quantum devices.

Initial measurement of beryllium-9 using high-resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences.

Precise and accurate determination of the ratio of the cosmogenic nuclide 10 Be to the stable isotope 9 Be (10 Be/9 Be) is needed across multiple fields of research within the Earth Sciences. Current techniques used to measure the 9 Be content of geological materials generally require a large amount of sample or solution aliquot and present a large range of analytical precisions. A range of geological reference materials underwent whole-rock dissolution and "strong" (0.04 M NH2 OH.HCl in 25% acetic acid) and "weak" (0.02 M NH2 OH.HCl in 10% acetic acid) leaching to represent a range of potential applications within the geosciences. After treatment, the 9 Be and major element (Na, Ca, Mg, Fe, Mn, Al and Ti) content of sample solutions were determined by high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) using a Thermo® ELEMENT XR instrument. The 9 Be concentration of whole-rock and leaching solutions displayed a wide range of values within each geological reference material, generally following a uniform relationship implying a potential kinetic control on NH2 OH leaching, as suggested by major element profiles. A precision of 0.1 to 1.4% is achieved independent of sample size or leaching strength. Initial results suggest that the use of HR-ICP-MS improves the precision of 9 Be analysis for a range of geological reference materials. A high precision is maintained despite reducing the sample size or strength of leaching solution. This has implications for the use of the Be isotope system within the Earth Sciences by reducing the propagated uncertainty of 10 Be/9 Be ratios or the mass of sample or 9 Be aliquot used.

Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard\u2013Jones potentials

The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy.

Impact of Sudden Global Events on Cross-Field Research Cooperation

Since the outbreak of COVID-19, in addition to the continuous increment in the number of infected patients, the number of COVID-19-related papers has also increased significantly. According to the statistics, its number even exceeds the research of some research fields over many years. Similar to COVID-19, the related research on COVID-19 also seems highly infectious. What causes this situation? By crawling the data of COVID-19-related papers from web of Sciences this year, we found that there are three mechanisms to promote the rapid growth of the number of COVID-19 papers: incentive mechanism, cross-field collaboration mechanism, and potential impact mechanism of writing papers. To understand the impact of COVID-19 on cross-domain paper network further, we proposed a new construction method of multi-field paper association structure network based on COVID-19. The paper association mechanism and the wall breaking principle between multiple research fields were found through the experiments. Then, combined with the constructed network, we gave the knowledge dissemination model of the new discoveries in multiple fields and obtained some relevant new findings.

A Partially Non-Compensatory Method to Measure the Smart and Sustainable Level of Italian Municipalities

A smart sustainable city (SSC) is a paradigm that encapsulates the latest lines of development in multiple fields of research. The attempt to converge towards a model of sustainable urban life, made difficult by increasing anthropic pressure and polluting activities conducted by man, is also reflected in the intentions of public institutions to take measures of environmental risk mitigation. The change towards more liveable cities must also include the adoption of more far-reaching measures in various sectors. The objective of our work was to provide an analysis in order to assess which of the Italian provincial municipalities were most closely related to the paradigm of SSCs. This aim was pursued through a comparison based on the results of a partially non-compensatory quantitative method, known as the Pena’s Distance method (DP2). The smartest and most sustainable cities, such as Siena, Milan and Padua, were not identified on the basis of common urban characteristics but rather derived from the combination of distinctive and functional elements in the pursuit of a strategic approach aimed at fully exploiting the resources of each area. Moreover, at a macro-geographical level, from the analysis emerged the presence of contiguous clusters, i.e., areas in which a major concentration of smart sustainable municipalities tended to form.

What do we know about the role of lncRNAs in multiple sclerosis?

Multiple sclerosis is a chronic, inflammatory and degenerative disease of the central nervous system of unknown aetiology although well-defined evidence supports an autoimmune pathogenesis. So far, the exact mechanisms leading to autoimmune diseases are still only partially understood. We know that genetic, epigenetic, molecular, and cellular factors resulting in pathogenic inflammatory responses are certainly involved. Long non-coding RNAs (lncRNAs) are non-protein coding transcripts longer than 200 nucleotides that play an important role in both innate and acquired immunity, so there is great interest in lncRNAs involved in autoimmune diseases. The research on multiple sclerosis has been enriched with many studies on the molecular role of lncRNAs in the pathogenesis of the disease and their potential application as diagnostic and prognostic biomarkers. In particular, many multiple sclerosis fields of research are based on the identification of lncRNAs as possible biomarkers able to predict the onset of the disease, its activity degree, its progression phase and the response to disease-modifying drugs. Last but not least, studies on lncRNAs can provide a new molecular target for new therapies, missing, so far, a cure for multiple sclerosis. While our knowledge on the role of lncRNA in multiple sclerosis has recently improved, further studies are required to better understand the specific role of lncRNAs in this neurological disease. In this review, we present the most recent studies on molecular characterization of lncRNAs in multiple sclerosis disorder discussing their clinical relevance as biomarkers for diagnosis and treatments.