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Composition, creative writing studies, and the digital humanities
\"In an era of blurred generic boundaries, multimedia storytelling, and open-source culture, creative writing scholars stand poised to consider the role that technology-and the creative writer's playful engagement with technology-has occupied in the evolution of its theory and practice. Composition, Creative Writing Studies and the Digital Humanities is the first book to bring these three fields together to open up new opportunities and directions for creative writing studies. Placing the rise of Creative Writing Studies alongside the rise of the digital humanities in Composition/Rhetoric, Adam Koehler shows that the use of new media and its attendant re-evaluation of fundamental assumptions in the field stands to guide Creative Writing Studies into a new era. Covering current developments in composition and the digital humanities, this book re-examines established assumptions about process, genre, authority/authorship and pedagogical practice in the creative writing classroom.\" -- Back cover.
Book
Genetic studies of body mass index yield new insights for obesity biology
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (
P
< 5 × 10
−8
), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis.
Genetic correlates of obesity
In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Journal Article
Mechanical metamaterials and beyond
Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities.
Mechanical metamaterials are known for their unconventional mechanical properties. In this perspective, the authors give an overview of the current state of mechanical materials research and suggest a roadmap for next-generation active and responsive mechanical metamaterials.
Journal Article
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm
The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at
https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho
.
Journal Article
Lithium extraction from low-quality brines
In the quest for environmental sustainability, the rising demand for electric vehicles and renewable energy technologies has substantially increased the need for efficient lithium extraction methods. Traditional lithium production, relying on geographically concentrated hard-rock ores and salar brines, is associated with considerable energy consumption, greenhouse gas emissions, groundwater depletion and land disturbance, thereby posing notable environmental and supply chain challenges. On the other hand, low-quality brines—such as those found in sedimentary waters, geothermal fluids, oilfield-produced waters, seawater and some salar brines and salt lakes—hold large potential owing to their extensive reserves and widespread geographical distribution. However, extracting lithium from these sources presents technical challenges owing to low lithium concentrations and high magnesium-to-lithium ratios. This Review explores the latest advances and continuing challenges in lithium extraction from these demanding yet promising sources, covering a variety of methods, including precipitation, solvent extraction, sorption, membrane-based separation and electrochemical-based separation. Furthermore, we share perspectives on the future development of lithium extraction technologies, framed within the basic principles of separation processes. The aim is to encourage the development of innovative extraction methods capable of making use of the substantial potential of low-quality brines.
Precipitation, solvent extraction, sorption, membrane-based separation and electrochemical-based separation are described as promising methods for extracting lithium from low-quality brines, which have extensive reserves and widespread geographical distributions.
Journal Article
Concepts of extracellular matrix remodelling in tumour progression and metastasis
Tissues are dynamically shaped by bidirectional communication between resident cells and the extracellular matrix (ECM) through cell-matrix interactions and ECM remodelling. Tumours leverage ECM remodelling to create a microenvironment that promotes tumourigenesis and metastasis. In this review, we focus on how tumour and tumour-associated stromal cells deposit, biochemically and biophysically modify, and degrade tumour-associated ECM. These tumour-driven changes support tumour growth, increase migration of tumour cells, and remodel the ECM in distant organs to allow for metastatic progression. A better understanding of the underlying mechanisms of tumourigenic ECM remodelling is crucial for developing therapeutic treatments for patients.
Tumors are more than cancer cells — the extracellular matrix is a protein structure that organizes all tissues and is altered in cancer. Here, the authors review recent progress in understanding how the cancer cells and tumor-associated stroma cells remodel the extracellular matrix to drive tumor growth and metastasis.
Journal Article
Ladderphane copolymers for high-temperature capacitive energy storage
For capacitive energy storage at elevated temperatures
1
–
4
, dielectric polymers are required to integrate low electrical conduction with high thermal conductivity. The coexistence of these seemingly contradictory properties remains a persistent challenge for existing polymers. We describe here a class of ladderphane copolymers exhibiting more than one order of magnitude lower electrical conductivity than the existing polymers at high electric fields and elevated temperatures. Consequently, the ladderphane copolymer possesses a discharged energy density of 5.34 J cm
−3
with a charge–discharge efficiency of 90% at 200 °C, outperforming the existing dielectric polymers and composites. The ladderphane copolymers self-assemble into highly ordered arrays by π–π stacking interactions
5
,
6
, thus giving rise to an intrinsic through-plane thermal conductivity of 1.96 ± 0.06 W m
−1
K
−1
. The high thermal conductivity of the copolymer film permits efficient Joule heat dissipation and, accordingly, excellent cyclic stability at elevated temperatures and high electric fields. The demonstration of the breakdown self-healing ability of the copolymer further suggests the promise of the ladderphane structures for high-energy-density polymer capacitors operating under extreme conditions.
A class of dielectric copolymers called ladderphanes is shown to outperform existing dielectric polymers and composites, with high discharged energy density and charge–discharge efficiency even at temperatures up to 200 °C.
Journal Article
Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice
The ice arches that usually develop at the northern and southern ends of Nares Strait play an important role in modulating the export of Arctic Ocean multi-year sea ice. The Arctic Ocean is evolving towards an ice pack that is younger, thinner, and more mobile and the fate of its multi-year ice is becoming of increasing interest. Here, we use sea ice motion retrievals from Sentinel-1 imagery to report on the recent behavior of these ice arches and the associated ice fluxes. We show that the duration of arch formation has decreased over the past 20 years, while the ice area and volume fluxes along Nares Strait have both increased. These results suggest that a transition is underway towards a state where the formation of these arches will become atypical with a concomitant increase in the export of multi-year ice accelerating the transition towards a younger and thinner Arctic ice pack.
Ice arches that form along Nares Strait, which separates Greenland and Ellesmere Island, act to reduce the export of thick multi-year ice out of the Arctic. Here, we show that there has been a recent trend towards shorter duration arch formation that has resulted in enhanced transport of ice along the strait.
Journal Article
On evaluation metrics for medical applications of artificial intelligence
Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.
Journal Article
Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction
Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The
k
-nearest neighbour (KNN) algorithm is the most frequently used among the wide range of machine learning algorithms. This paper presents a study on different KNN variants (Classic one, Adaptive, Locally adaptive, k-means clustering, Fuzzy, Mutual, Ensemble, Hassanat and Generalised mean distance) and their performance comparison for disease prediction. This study analysed these variants in-depth through implementations and experimentations using eight machine learning benchmark datasets obtained from Kaggle, UCI Machine learning repository and OpenML. The datasets were related to different disease contexts. We considered the performance measures of accuracy, precision and recall for comparative analysis. The average accuracy values of these variants ranged from 64.22% to 83.62%. The Hassanaat KNN showed the highest average accuracy (83.62%), followed by the ensemble approach KNN (82.34%). A relative performance index is also proposed based on each performance measure to assess each variant and compare the results. This study identified Hassanat KNN as the best performing variant based on the accuracy-based version of this index, followed by the ensemble approach KNN. This study also provided a relative comparison among KNN variants based on precision and recall measures. Finally, this paper summarises which KNN variant is the most promising candidate to follow under the consideration of three performance measures (accuracy, precision and recall) for disease prediction. Healthcare researchers and stakeholders could use the findings of this study to select the appropriate KNN variant for predictive disease risk analytics.
Journal Article