The sixth version of the Model for Interdisciplinary Research on Climate
(MIROC), called MIROC6, was cooperatively developed by a Japanese modeling
community. In the present paper, simulated mean ...climate, internal
climate variability, and climate sensitivity in MIROC6 are evaluated and
briefly summarized in comparison with the previous version of our climate
model (MIROC5) and observations. The results show that the overall
reproducibility of mean climate and internal climate variability in MIROC6
is better than that in MIROC5. The tropical climate systems (e.g.,
summertime precipitation in the western Pacific and the eastward-propagating
Madden–Julian oscillation) and the midlatitude atmospheric circulation
(e.g., the westerlies, the polar night jet, and troposphere–stratosphere
interactions) are significantly improved in MIROC6. These improvements can
be attributed to the newly implemented parameterization for shallow
convective processes and to the inclusion of the stratosphere. While there
are significant differences in climates and variabilities between the two
models, the effective climate sensitivity of 2.6 K remains the same because
the differences in radiative forcing and climate feedback tend to offset
each other. With an aim towards contributing to the sixth phase of the
Coupled Model Intercomparison Project, designated simulations tackling a
wide range of climate science issues, as well as seasonal to decadal climate
predictions and future climate projections, are currently ongoing using
MIROC6.
As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in ...biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.
To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence.
A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method.
The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models.
A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.
Nanomechanical devices have attracted the interest of a growing interdisciplinary research community, since they can be used as highly sensitive transducers for various physical quantities. Exquisite ...control over these systems facilitates experiments on the foundations of physics. Here, we demonstrate that an optically trapped silicon nanorod, set into rotation at MHz frequencies, can be locked to an external clock, transducing the properties of the time standard to the rod's motion with a remarkable frequency stability f
/Δf
of 7.7 × 10
. While the dynamics of this periodically driven rotor generally can be chaotic, we derive and verify that stable limit cycles exist over a surprisingly wide parameter range. This robustness should enable, in principle, measurements of external torques with sensitivities better than 0.25 zNm, even at room temperature. We show that in a dilute gas, real-time phase measurements on the locked nanorod transduce pressure values with a sensitivity of 0.3%.
Biopolymers are a leading class of functional material suitable for high-value applications and are of great interest to researchers and professionals across various disciplines. Interdisciplinary ...research is important to understand the basic and applied aspects of biopolymers to address several complex problems associated with good health and well-being. To reduce the environmental impact and dependence on fossil fuels, a lot of effort has gone into replacing synthetic polymers with biodegradable materials, especially those derived from natural resources. In this regard, many types of natural or biopolymers have been developed to meet the needs of ever-expanding applications. These biopolymers are currently used in food applications and are expanding their use in the pharmaceutical and medical industries due to their unique properties. This review focuses on the various uses of biopolymers in the food and medical industry and provides a future outlook for the biopolymer industry.
Nanotechnology has played an important role in drug delivery and biomedical imaging over the past two decades. In particular, nanoscale metal–organic frameworks (nMOFs) are emerging as an important ...class of biomedically relevant nanomaterials due to their high porosity, multifunctionality, and biocompatibility. The high porosity of nMOFs allows for the encapsulation of exceptionally high payloads of therapeutic and/or imaging cargoes while the building blocks—both ligands and the secondary building units (SBUs)—can be utilized to load drugs and/or imaging agents via covalent attachment. The ligands and SBUs of nMOFs can also be functionalized for surface passivation or active targeting at overexpressed biomarkers. The metal ions or metal clusters on nMOFs also render them viable candidates as contrast agents for magnetic resonance imaging, computed tomography, or other imaging modalities. This review article summarizes recent progress on nMOF designs and their exploration in biomedical areas. First, the therapeutic applications of nMOFs, based on four distinct drug loading strategies, are discussed, followed by a summary of nMOF designs for imaging and biosensing. The review is concluded by exploring the fundamental challenges facing nMOF‐based therapeutic, imaging, and biosensing agents. This review hopefully can stimulate interdisciplinary research at the intersection of MOFs and biomedicine.
Nanoscale metal–organic frameworks (nMOFs) comprise an emerging class of biomedically important nanomaterials due to their high porosity, multifunctionality, and biocompatibility. Recent progress of nMOF designs and their applications in areas of therapeutics, imaging, and biosensing are summarized, and the fundamental challenges in current nMOF biomedical research are explored.
Mechanochemistry with solvent‐free and environmentally friendly characteristics is one of the most promising alternatives to traditional liquid‐phase‐based reactions, demonstrating epoch‐making ...significance in the realization of different types of chemistry. Mechanochemistry utilizes mechanical energy to promote physical and chemical transformations to design complex molecules and nanostructured materials, encourage dispersion and recombination of multiphase components, and accelerate reaction rates and efficiencies via highly reactive surfaces. In particular, mechanochemistry deserves special attention because it is capable of endowing energy materials with unique characteristics and properties. Herein, the latest advances and progress in mechanochemistry for the preparation and modification of energy materials are reviewed. An outline of the basic knowledge, methods, and characteristics of different mechanochemical strategies is presented, distinguishing this review from most mechanochemistry reviews that only focus on ball‐milling. Next, this outline is followed by a detailed and insightful discussion of mechanochemistry‐involved energy conversion and storage applications. The discussion comprehensively covers aspects of energy transformations from mechanical/optical/chemical energy to electrical energy. Finally, next‐generation advanced energy materials are proposed. This review is intended to bring mechanochemistry to the frontline and guide this burgeoning field of interdisciplinary research for developing advanced energy materials with greener mechanical force.
Mechanochemistry as a powerful tool to prepare advanced energy materials is reviewed, covering specific aspects of energy transformations from mechanical/optical/chemical energy to electrical energy. This review intends to comprehensively clarify mechanism, features, and methods of mechanochemistry and summarize the latest advances and progress in this field to inspire interdisciplinary research for developing novel energy materials with greener mechanical force.
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in ...Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.
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