bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated estimation of individualized organ-specific dose and noise from clinical CT scans
Authors: Wang Sen , Medrano Maria Jose , Imran Abdullah Al Zubaer , Lee Wonkyeong , Cao Jennie Jiayi , Stevens Grant M , Tse Justin Ruey , Wang Adam S
Published: January 2025

Objective. Radiation dose and diagnostic image quality are opposing constraints in x-ray computed tomography (CT). Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans. Approach. To estimate organ-specific dose and noise, we compute dose maps, noise maps at desired dose levels and organ segmentations. In our pipeline, dose maps are generated using Monte Carlo simulation. The noise map is obtained by scaling the inserted noise in synthetic low-dose emulation in order to avoid anatomical structures, where the scaling coefficients are empirically calibrated. Organ segmentations are generated by a deep learning-based method (TotalSegmentator). The proposed noise model is evaluated on a clinical dataset of 12 CT scans, a phantom dataset of 3 uniform phantom scans, and a cross-site dataset of 26 scans. The accuracy of deep learning-based segmentations for organ-level dose and noise estimates was tested using a dataset of 41 cases with expert segmentations of six organs: lungs, liver, kidneys, bladder, spleen, and pancreas. Main results. The empirical noise model performs well, with an average RMSE approximately 1.5 HU and an average relative RMSE approximately 5% across different dose levels. The segmentation from TotalSegmentator yielded a mean Dice score of 0.8597 across the six organs (max = 0.9315 in liver, min = 0.6855 in pancreas). The resulting error in organ-level dose and noise estimation was less than 2% for most organs. Significance. The proposed pipeline can output individualized organ-specific dose and noise estimates accurately for personalized protocol evaluation and optimization. It is fully automated and can be scalable to large clinical datasets. This pipeline can be used to optimize image quality for specific organs and thus clinical tasks, without adversely affecting overall radiation dose.
Generalized, sublethal damage-based mathematical approach for improved modeling of clonogenic survival curve flattening upon hyperthermia, radiotherapy, and beyond
Authors: De Mendoza Adriana M , Michlíková Soňa , Castro Paula S , Muñoz Anni G , Eckhardt Lisa , Lange Steffen , Kunz-Schughart Leoni A
Published: January 2025

Objective. Mathematical modeling can offer valuable insights into the behavior of biological systems upon treatment. Different mathematical models (empirical, semi-empirical, and mechanistic) have been designed to predict the efficacy of either hyperthermia (HT), radiotherapy (RT), or their combination. However, mathematical approaches capable of modeling cell survival from shared general principles for both mono-treatments alone and their co-application are rare. Moreover, some cell cultures show dose-dependent saturation in response to HT or RT, manifesting in survival curve flattenings. An advanced survival model must, therefore, appropriately reflect such behavior. Approach. We propose a mathematical approach to model the effect of both treatments based on the general principle of sublethal damage (SLD) accumulation for the induction of cell death and irreversible proliferation arrest. Our approach extends Jung’s model on heat-induced cellular inactivation by incorporating dose-dependent recovery rates that delineate changes in SLD restoration. Main results. The resulting unified model (Umodel) accurately describes HT and RT survival outcomes, applies to simultaneous thermoradiotherapy modeling, and is particularly suited to reproduce survival curve flattening phenomena. We demonstrate the Umodel’s robust performance (R2 0.95) based on numerous clonogenic cell survival data sets from the literature and our experimental studies. Significance. The proposed Umodel allows using a single unified mathematical function based on generalized principles of accumulation of SLD with implemented radiosensitization, regardless of the type of energy deposited and the mechanism of action. It can reproduce various patterns of clonogenic survival curves, including any flattening, thus encompassing the variability of cell reactions to therapy, thereby potentially better reflecting overall tumor responses. Our approach opens a range of options for further model developments and strategic therapy outcome predictions of sequential treatments applied in different orders and varying recovery intervals between them.
Dispersive gains enhance wireless power transfer with asymmetric resonance
Authors: Hao Xianglin , Yin Ke , Cai Shiqing , Zou Jianlong , Wang Ruibin , Ma Xikui , Tse Chi Kong , Dong Tianyu
Published: January 2025

Parity-time (PT) symmetry is a fundamental concept in non-Hermitian physics that has recently gained attention for its potential in engineering advanced electronic systems and achieving robust wireless power transfer (WPT) even in the presence of disturbances, through the incorporation of nonlinearity. However, the current PT-symmetric scheme falls short of achieving the theoretical maximum efficiency of WPT and faces challenges when applied to non-resistive loads. In this study, we propose a theoretical framework and provide experimental evidence demonstrating that asymmetric resonance, based on dispersive gain, can greatly enhance the efficiency of WPT beyond the limits of symmetric approaches. By leveraging the gain spectrum interleaving resulting from dispersion, we observe a mode switching phenomenon in asymmetric systems similar to the symmetry-breaking effect. This phenomenon reshapes the distribution of resonance energy and enables more efficient WPT compared to conventional methods. Our findings open up new possibilities for harnessing dispersion effects in various domains such as electronics, microwaves, and optics. This work represents a significant step towards exploiting dispersion as a means to optimize WPT and lays the foundation for future advancements in these fields.
Uncoiling deployment of thin-shell composite booms with piezoelectric actuation
Authors: Daye Jacob G , Lee Andrew J
Published: January 2025

The efficacy of using piezoelectric actuators to initiate the dynamic deployment of bistable composite tape springs is evaluated in this paper. Ultra-thin composite booms such as tape springs and their cross-sectional variants have seen increased popularity in spacecraft structures due to enabling the precise deployment of flexible solar arrays, sails, reflectors, and antennas. They can elastically transition between the deployed ‘extended’ position and the stowed ‘coiled’ position while retaining superior stiffness, thermal properties, mass efficiency, and compactness when compared to thin-shelled metal booms and rigid articulated columns. Bistability in the coiled and extended states allows the boom to exhibit more controllable self-deployment and become reconfigurable, which could allow spacecraft to relocate, redeploy, and adapt to changing environmental conditions or mission objectives. Deployment systems commonly include motors and mechanical restraints that significantly contribute to mechanical complexity and spacecraft weight. Since bistable booms do not rely on elastic instability of packaging to initiate motion, a non-intrusive and lightweight actuation mechanism is needed to trigger deployment. This paper experimentally demonstrates how a macro fiber composite actuator can statically and dynamically excite a stowed composite tape spring to initiate unrolling into its extended state. A finite element model in Abaqus/Explicit is also developed to computationally corroborate the deployment schemes discussed.
Flexible biomimetic olfactory neurons based on organic heterojunction
Authors: Feng Tianyang , Meng Jialin , Xu Hang , Yang Yafen , Wang Tianyu , Zhu Hao , Sun Qingqing , Zhang David Wei , Chen Lin
Published: January 2025

Simulating the human olfactory nervous system is one of the key issues in the field of neuromorphic computing. Olfactory neurons interact with gas molecules, transmitting and storing odor information to the olfactory center of the brain. In order to emulate the complex functionalities of olfactory neurons, this study presents a flexible olfactory synapse transistor (OST) based on pentacene/C8-BTBT organic heterojunction. By modulating the interface between the energy bands of the organic semiconductor layers, this device demonstrates high sensitivity (ppb level) and memory function for NH3 sensing. Typical synaptic behaviors triggered by NH3 pulses have been successfully demonstrated, such as inhibitory postsynaptic currents (IPSC), paired-pulse depression (PPD), long-term potentiation/depression (LTP/LTD), and transition from short-term depression (STD) to long-term depression (LTD). Furthermore, this device maintains stable olfactory synaptic functions even under different bending conditions, which can present new insights and possibilities for flexible synaptic systems and bio-inspired electronic products.
The impact of plasmonic Ag−Au bimetallic nanoparticles on photocurrent enhancement in GaN-based photodetectors
Authors: Vu Thi Kim Oanh , Bui Thi Thu Phuong , Nguyen Ngoc Anh , Nguyen Thi Thanh Bao , Nguyen Thi Minh Hien , Kim Eun Kyu
Published: January 2025

Recently, there has been considerable interest in high-efficiency ultraviolet (UV) photodetectors for their potential practical uses. In this study, a high-quality UV photodetector was fabricated using a combination of Ag and Au NPs with GaN film. The GaN film was deposited using sputtering technique, whereas Ag and Au films were grown using thermal evaporation technique. Ag−Au bimetallic nanoparticles were formed by treating them at the various annealing temperature to improve the interaction between light and the photoactive layers of the photodetectors. The optimal annealing temperature to achieve the best performance of a photodetector is 650 °C. This led to a photoresponsivity of 98.5 A/W and the ON/OFF ratio of 705 at low bias voltage of 1 V. This work establishes the foundation for the advancement of high-performance UV photodetectors.
Ultrabroadband THz probing of anisotropic optical conductivity and plasmonic damping in graphene nanostructures
Authors: Ji Jie , Xiang Cheng , Gammelgaard Lene , Shivayogimath Abhay , Zhou Binbin , Jepsen Peter Uhd , Bøggild Peter
Published: January 2025

Graphene nanostructures have garnered significant attention in the terahertz (THz) range due to their exceptional plasmonic properties. Exploring the THz conductivity of graphene nanostructures is crucial for optimizing their performance in advanced devices, necessitating comprehensive research into their optical responses across a broad frequency spectrum. In this study, we experimentally investigate the optical conductivity of graphene nanostructures using ultra-broadband THz time-domain spectroscopy. Extracted from the time-dependent THz echo signals, an order-of-magnitude enhancement of localized surface plasmon resonances (LSPRs) in graphene nanostructures was observed. This led to the anisotropic behavior in optical conductivity over the broadband THz range. In addition, the analyzed results show that LSPRs in graphene nanostructures experience damping loss from the intraband transition, resulting in energy dissipation into the substrate through the dielectric space layer. The induced redshift of the LSPR and linewidth broadening, in turn, modifies the optical conductivity of the graphene nanostructures. These findings provide valuable insights for developing advanced tunable THz devices based on graphene nanostructures.
Let’s hear it for NPL
Authors: Wallard Andrew
Published: January 2025

In response to the Transactions article “Spin-offs from big science” by James McKenzie (October 2024).
The triadic relationship between green HRM, innovation, and pro-environmental behaviour: a study of their interactions and impacts on employee productivity and organizational sustainability
Authors: Gazi Md. Abu Issa , Masud Abdullah Al , Emon Md. , Ibrahim Md. , Senathirajah Abdul Rahman bin S
Published: January 2025

The study aims to examine the impact of Green Human Resource Management (GHRM) and eco-consciousness on work productivity, eventually resulting in greater efficiency and sustainability. Furthermore, we are also investigating the role of green innovation, environmentally conscious behavior, and employee productivity as mediators between green HRM and societal, economic, and environmental outcomes. Through the use of an online survey, 401 samples were collected from a variety of industries in Bangladesh, and then structural equation modeling was used to analyses the data. The study found that green HRM, innovation, and pro-environmental behavior favorably and significantly influence worker productivity. Furthermore, worker productivity directly and statistically affects social, economic, and environmental performance. The originality of this study resides in its comprehensive illustration of how GHRM, green innovation, and pro-environmental behavior effectively encourage worker productivity, resulting in organizational sustainability.
Li+ diffusion in crystalline lithium silicides: influence of intrinsic point defects
Authors: Kirsch Christoph , Dreßler Christian , Sebastiani Daniel
Published: January 2025

Crystalline lithium silicides Li
x
Si
y
are a group of Zintl-like compounds, of which Li15Si4 is observed crystallizing in Si anodes after complete electrochemical lithiation. Here, Li diffusion in Li1Si1, Li12Si7, Li13Si4 and Li15Si4 is extensively studied by means of ab initio molecular dynamics simulations and nudged elastic band calculations considering various intrinsic point defects. Li interstitials are identified as the main vehicle of diffusion due to typically low formation and migration energies. They diffuse via an interstitialcy mechanism usually involving 2 or 3 atoms. Moreover, Frenkel defects are found to play a role in Li12Si7 and Li13Si4 only, as their formation energies are relatively high in Li1Si1 and Li15Si4. All investigated intrinsic defects have an impact on energy barriers of nearby as well as farther diffusion paths.
Dynamic characteristics analysis of high-speed motorized spindle system considering unbalanced magnetic pull and non-linear bearing restoring force
Authors: Zhang Xiangming , Ma Zhimin , Fang Miaofeng , Jiang Yongying
Published: January 2025

The high-speed motorized spindles play a pivotal role in modern machining, holding vast application potential and profound research significance. To analyze the dynamic characteristics of the high-speed motorized spindle system, an fourteen-degree-of-freedom dynamic model has been established using the lumped parameter method, and numerical methods have been employed for computation. Then, the bearing stiffness, the variation of the restoring force, and the unbalanced magnetic force are considered. Additionally, the parameters of the model were optimized using the genetic algorithm. Finally, the effects of spindle speed, initial eccentricity, and bearing parameters on the vibration behavior and stability of the motorized spindle were analyzed using bifurcation diagrams, time-frequency waveform, spectra, trajectories, and Poincaré. The study provides a valuable reference for the design and optimization of motorized spindle systems.
Predictive modeling of transverse cracking in continuously reinforced concrete pavement: a machine learning approach
Authors: Alnaqbi Ali , Al-Khateeb Ghazi G , Zeiada Waleed
Published: January 2025

Accurate prediction of transverse cracking in Continuously Reinforced Concrete Pavement (CRCP) is critical for improving infrastructure management procedures and preserving the road network’s long-term durability and safety. This paper conducts a thorough analysis into predicting transverse cracking in CRCP using machine learning approaches. The research involved meticulous data preparation, feature selection, and evaluation of various machine learning models to identify the most effective predictor. Key variables such as pavement age, total thickness, temperature, freeze index, traffic volume, precipitation, and initial International Roughness Index (IRI) were analyzed for their impact on transverse cracking occurrences. Sensitivity analysis was conducted to assess the influence of individual input variables on model predictions. Results indicated that the cubic Support Vector Machine (SVM) model outperformed other models, demonstrating exceptional predictive accuracy. Furthermore, sensitivity analysis revealed significant correlations between input variables and transverse cracking occurrences, emphasizing the importance of considering a holistic range of factors in pavement engineering and maintenance strategies. Our findings, which provide insights into the intricate interactions between input factors and pavement distress, help to create tailored treatments and methods, such as optimized crack sealing schedules, improved reinforcement strategies, and the use of high-performance materials, for minimizing transverse cracking and enhancing CRCP performance.
CFNet:CSI compression feedback network based on WiFi sensing
Authors: He Yuting , Wei Mingjun , Li Dapeng , Li Pinhao , Li Hui
Published: January 2025

To solve the problem of huge channel state information (CSI)data caused by a large number of antennas in large-scale MIMO systems,a CSI compressed feedback network model (CFNet)with encoder-decoder structure is proposed in this paper. Specifically, the encoder efficiently compresses the CSI data to reduce the burden of data transmission and storage. A key component, the Asymmetric Expansion Convolution (AEC) module, combines asymmetric convolution and dilated convolution to extract feature information from different directions, thereby increasing the model’s receptive field and minimizing information loss. The compressed CSI is fed into a Gated Recurrent Unit (GRU), which captures long-term dependencies in the CSI sequence using the encoder’s output and the hidden state of the upper window. Additionally, on the decoder side, the proposed Asymmetric Transpose Convolution reconstructs the compressed CSI data, enhancing the network’s ability to capture and express CSI features through two complementary convolution operations, ultimately reducing communication costs. Thanks to the proposed encoder and decoder, CFNet can accurately identify fine-grained features of human activities recognition(HAR) while leveraging compressed sensing techniques, further improving data processing efficiency and recognition accuracy. The experiments conducted on two public datasets demonstrate that the evaluation metrics of the proposed network outperform those of advanced methods in the relevant field.
Infectious disease time series modelling using transformer self-attention based network
Authors: Prakash Satya , Jalal Anand Singh , Pathak Pooja
Published: January 2025

Researchers are focusing on improving time series forecasting methods to address real-world problems like COVID-19. Current methods show decreased accuracy due to unpredictable seasonality, and enhancing models to handle long-term dependencies is crucial for better forecasting accuracy. This paper presents a Transformer self-attention based novel approach for infectious disease time series forecasting, specifically for COVID-19. The proposed method utilizes the Ensemble Empirical Mode Decomposition (EEMD) and Local Outlier Factor (LOF) methods for data pre-processing and to detect outliers. Next, a modified self-attention model based on Transformer neural network is introduced for predicting COVID-19 time series forecasting for the first time. The research specifically investigates the application of encoder/decoder networks with an enhanced Positional Encoding approach. This involves using a novel time encoding technique on the input pattern to achieve more precise intended output. Consequently, the parameters in the Transformer model are adjusted using the Arithmetic Optimization Algorithm (AOA) to enhance the accuracy of the prediction. The model generates more accurate predictions over broader time intervals, with the lowest MAE of 371.92 and RMSE of 674.61, indicating superior predictive accuracy by approximately 30% compared to other state-of-the-art methods. The proposed Transformer model has demonstrated significant improvements in robustness and forecasting accuracy compared to standard approaches such as LSTM, RNN, Exponential Smoothing, AutoARIMA, and TBATS for the COVID-19 time series of India, USA, and Brazil. The suggested model, due to its superior predictive accuracy, is applicable in diverse time series forecasting domains such as stock market trends, sales, and industrial consumption forecasting etc.
Huxley
Authors: Klein Alexander Mugar
Published: January 2025

This chapter examines the 1870s automatism controversy with a focus on T.H. Huxley. It reconstructs his central argument for epiphenomenalism, his favored form of automatism. His argument depended on the mechanistic idea that physiological explanation should only appeal to physical and chemical causes. Huxley supported the plausibility of this mechanistic program by appealing to purposive behavior in decapitated frogs, creatures he took to be unconscious. In contrast, anti-automatist opponents like G. H. Lewes had premised their arguments on a rival interpretation of the decapitated frog experiments according to which such creatures retain sentience. When the two sides stalemated, one cause was an inability to resolve rival interpretations of these experiments. This chapter uses the nineteenth-century dispute over decapitated frogs to develop a philosophical analysis of the so-called measurement problem, which is the problem of determining empirically whether a given creature is conscious.
Moral Anti-Exceptionalism
Authors: Williamson Timothy
Published: January 2025

The chapter opens with a manifesto for anti-exceptionalism in metaethics, on which evaluative terms refer to properties and relations of the same general kind as non-evaluative terms, which can be known about in the same general way: the moral is unexceptional in its metaphysics, epistemology, and semantics. An intensional framework for general semantics is explained and defended. Using that framework, errors are identified in leading representatives of critiques of moral realism: Olson’s metaphysical argument for moral error theory and Street’s evolutionary argument against moral theories of value. In particular, it is shown how moral and non-moral terms can refer to the same properties and relations. It is also briefly shown how realism about global values is a familiar kind of development out of realism about local values.
Justifications, Excuses, and Sceptical Scenarios
Authors: Williamson Timothy
Published: January 2025

This chapter argues that epistemologists have misapplied the term ‘justified’ by relying on an impoverished menu of normative options, and in particular by neglecting the category of excuses, for example when classifying as justified the false beliefs of victims of sceptical scenarios. More generally, any norm is shown to generate various logically independent derivative norms, such as being disposed to comply with the primary norm, and doing what someone disposed to comply with the primary norm would do. These distinctions are illustrated with respect to norms of keeping promises and obeying the law. Any norm of fitting one’s belief to one’s evidence is shown to be violated in some cases by agents disposed to comply with it. That victims of sceptical scenarios believe in accord with laudable cognitive dispositions does not show that they are complying with the primary norm of belief.
Whose Solidarity?
Authors: Crowder Chaya Y
Published: January 2025

Chapter 3 examines the various properties of the intersectional solidarity index. This chapter introduces the primary data set: the Intersectional Politics Study. This chapter begins as an exercise in construct validation. A series of validation checks was conducted to determine that the intersectional solidarity index measures what it is intended to measure. In doing so, the measure was found to be internally consistent. While the intersectional solidarity index is related to broad pro-social orientations like altruism, racial sympathy, and liberal ideology, the orientation it captures is also distinct from broad pro-social orientations of altruism, group empathy, and liberal ideology. The characteristics of people who are most likely to exhibit this disposition were examined and the index finds that intersectional solidarity is higher among people of color, and lesbian, gay, and bisexual people. There is also a particularly strong relationship between recognizing that one has experienced discrimination and exhibiting intersectional solidarity.
Don’t Thank Me, Show Me
Authors: Crowder Chaya Y
Published: January 2025

This chapters turns to examining political outcomes and policy preferences that have previously been left out of mainstream discussion of Black politics and gender and American politics. Chapter 4 considers the influence of intersectional solidarity on public opinion. Using the Intersectional Politics Study, intersectional solidarity is found to be consistently and positively associated with support for policies that explicitly or implicitly benefit Black women. While intersectional solidarity can influence anyone’s willingness to support intersectionally marginalized groups, the findings demonstrate that intersectional solidarity has an especially large payoff when it comes to the attitudes of white men because, relative to Black men and women and white women, they are the group who is least likely to support these issues, and therefore have the largest potential for growth. In particular, the chapter examines the relationship between intersectional solidarity and support for addressing the pay gap, the Black maternal healthcare crisis, domestic violence, and Black LGBTQ+ youth homelessness. I also evaluate the relationship between intersectional solidarity and support for a policy that requires employers to have designated spaces for breastfeeding in the workplace. Doing so demonstrates that intersectional solidarity has a less influential relationship when it comes to addressing policies that affect broader marginalized groups as opposed to policies that specifically affect disadvantaged marginalized subgroups.
Learning through Formal Instruction
Authors: Xiang Xin
Published: January 2025

The dominant form of learning in China and most other modern societies, Learning through Formal Instruction (Chapter 4), is a characteristic of hierarchical societies with sophisticated systems of abstract knowledge, although mass participation in this paradigm of learning is a modern phenomenon. Through her vivid descriptions of classroom interactions, the author demonstrates that the objective, style, and intensity of formal instruction varied drastically between the elite school and the three schools serving the popular classes. Despite shared focus on exam preparation, the former emphasized conceptual understanding while the latter relied heavily on memorization and corporal punishment. Channeling children toward different trajectories and identities, these differential practices contribute to the widening divide between metropolitan elites and the popular classes in modern societies.
Learning in Organized Activities
Authors: Xiang Xin
Published: January 2025

A means to inculcate status-marking dispositions in elite children in ancient civilizations, Learning in Organized Activities (Chapter 5) has become an important tool for marking the distinction of elite youth and preparing them for global competition and collaboration. With riveting stories from various organized activities, the author demonstrates the tremendous labor that prestigious public schools and elite parents put in behind the scenes to guide children’s participation in these activities and help them build impressive résumés. Ironically, even seemingly equity-oriented organized activities reinforce the intellectual and moral superiority of elite children over their less-privileged peers. In contrast, although the other three schools serving the popular classes also organized Art Festivals and Sports Meetings, they rarely had the resources or motivation to make these activities meaningful learning experiences for children.
Learning through Play
Authors: Xiang Xin
Published: January 2025

Chapter 6 (Learning through Play) illustrates what and how children learned from different forms of play across the four communities and teases out the economic, political, and social conditions that enable and constrain their play. In the organic learning communities emerging from sustained gameplay in and around schools, children acquired physical and mental competencies as well as built relationships with peers. Through playing digital games, children became seasoned consumers and flexible laborers of the digital entertainment industry. Moreover, opportunities for different forms of play are far from evenly distributed, which contributes to the production and reproduction of social boundaries along the lines of gender and class.
Beyond Donbas
Authors: Kudelia Serhiy
Published: January 2025

This chapter examines the reasons for the failure of the separatist movement in two regional centers outside Donbas—Kharkiv and Odesa—in spring 2014. It examines Russia’s tactics and objectives in these regions and how they differed from those employed in Donbas and Crimea. It also analyzes the composition of local separatist groups and the dynamics of pro-Russian mobilization as well as the strength of the countermobilization in defense of Ukraine’s integrity organized by local activists and political leaders. The chapter demonstrates that the decisions of municipal authorities to abstain from cooperation with separatists, the strong countermobilization potential of local pro-Ukrainian forces, and Russia’s unwillingness to commit significant resources to support disjointed separatist groups in these cities tipped the balance against pro-Russian activists.
Exemplary Citizens and the Symbolic Politics of Nonviolent Protest
Authors: Mantena Karuna
Published: January 2025

Recent studies of nonviolent protest across the twentieth and twenty-first centuries have demonstrated and celebrated the power of mass mobilization to undermine state legitimacy. Less attention has been paid to the rhetorical and symbolic power of nonviolent politics. Starting with the 2019 anti-CAA (Citizenship Amendment Act) protests in India, this chapter explores the ways in which nonviolent protest creates dissent to convey the meaning of a political message. The contemporary Indian example is used to recall some of the core ideas and practices of Gandhi and King about how nonviolent protest tries to move public opinion. The chapter suggests that, in their disobedience, Gandhi and King were dramatizing scenes of exemplary citizenship. Such scenes were devised to provoke a crisis of identity as the means by which to shift political beliefs and alignments.
Postscript
Authors: Metcalf James
Published: January 2025

The Postscript concludes the book by returning to the churchyard in essays and poems by William Wordsworth. These texts reveal how, for Wordsworth as for Gray, the churchyard is a landscape in which suffering the burden of history can be displaced, since it seems to conserve ways of life in the landscape elsewhere subject to violent rearrangement or decay. The Postscript reads this work for its complication of the apparent ease with which a turn to nature from industrial capital can take place as a strategy of consolation; it also looks to Wordsworth by way of contrast to the poets read in the preceding chapters, who refuse even this ambivalent relation to the stark, painful realities the churchyard reveals. It then summarizes the book’s principal claims: that the churchyard in eighteenth-century poetry—familiar but therefore taken for granted and misinterpreted as an innocuous backdrop—was a site of suffering on a massive social scale. In churchyard poetry, formerly habitual lines of continuity across generations gathered in space by this landscape’s symbolic centre of gravity are traumatically dispersed. Churchyard poetics is, therefore, against closure, without resolution, since the arrangements of thought and feeling variously expressed by the book’s churchyard poets remain anxious, anguished, and powerfully vocal. For the poets in Churchyard Poetics, the churchyard is a landscape of unceasing labour; their poems bring up and work through the legacies contained in this unsettled terrain, leaving us the textual remains of an alternative tradition of churchyard poetry.
Trends and considerations in the pursuit of postgraduate training: Motivations, barriers, and well-being
Authors: Torres Brittany Palasik , Aguiniga Ashlyn , Yzaguirre Storee , Thompson Sydney R , Wesling Megan
Published: January 2025

Purpose

Pharmacy residency training, endorsed by the American Society of Health-System Pharmacists (ASHP), is designed to enhance clinical pharmacy practice. However, in 2022 and subsequent years, the ASHP Resident Matching Program reported unprecedented numbers of unfilled positions, notably in postgraduate year 2 (PGY2) programs. This review explores motivations, barriers, and well-being indices in pharmacy residency pursuit and training, seeking to explain the rise in unfilled postgraduate training positions.

Methods

Two literature searches were performed using relevant resources and databases: search 1, to determine motivating factors and barriers to pursuit of postgraduate training; and search 2, to gather evidence related to resident well-being, burnout, and resiliency.

Results

Search 1 yielded 11 studies about motivating or deterring factors for pharmacy residency pursuit, whereas search 2 produced 16 articles on resident well-being. Major motivators for pursuing postgraduate training included the desire to gain knowledge and specialized training, recognition of evolving pharmacist roles, and viewing residency as a prerequisite for certain positions. Conversely, major barriers included financial or family obligations and concerns about job availability following graduation. Most articles discussed student pursuit of PGY1 residencies. Resident well-being encompassed burnout assessments, other wellness indicators like depression, and the impact of the coronavirus disease 2019 (COVID-19) pandemic. Developed well-being programs elicited mostly positive perceptions from pharmacy residents.

Conclusion

There is a body of published literature elucidating motivations and barriers to the pursuit of postgraduate training and well-being related to wellness/satisfaction on the job. However, to comprehensively evaluate well-being program impact and address gaps in literature regarding barriers to and motivators for pursuit of specialized PGY2 programs, further research is needed.
Taming the wild: domesticating untapped northern fruit tree and shrub resources in the era of high-throughput technologies
Authors: Gélinas Bélanger Jérôme
Published: January 2025

New crop`s need to emerge to provide sustainable solutions to climate change and increasing abiotic and biotic constraints on agriculture. A large breadth of northern fruit trees and shrubs exhibit a high potential for domestication; however, obstacles to implementing traditional breeding methods have hampered or dissuaded efforts for improvement. This review article proposes a unique roadmap for de novo domestication of northern fruit crops, with a focus on biotechnological (e.g. genome editing, rapid cycle breeding, and in planta transformation) approaches that can boast rapid evolutionary gains. In addition, numerous biotechnological (e.g. virus-induced flowering and grafting-mediated flowering) and breeding strategies (e.g. adaptation of speed breeding to fruit trees) that can hasten the transition from juvenility to sexual maturity are described. A description of an accelerated genetic breeding strategy with insights for 16 underutilized species (e.g. shagbark hickory, running serviceberry, horse chestnut, and black walnut) is provided to support their enhancement. Deemed unrealistic only a decade ago, progress in the realm of bioengineering heralds a future for northern orphan crops through the implementation of fast-tracked crop improvement programs. As such, the roadmap presented in this article paves the way to integrating these novel biotechnological discoveries and propel the development of these forgotten crops in a sustainable and timely manner.
Q&A Audie Muller
Authors: Bowler S , Muller A
Published: January 2025

The Head of Operations at the RAS talks challenges, change – and horses
m6A RNA modification pathway: orchestrating fibrotic mechanisms across multiple organs
Authors: Huang Xiangfei , Yu Zilu , Tian Juan , Chen Tao , Wei Aiping , Mei Chao , Chen Shibiao , Li Yong
Published: January 2025

Organ fibrosis, a common consequence of chronic tissue injury, presents a significant health challenge. Recent research has revealed the regulatory role of N6-methyladenosine (m6A) RNA modification in fibrosis of various organs, including the lung, liver, kidney, and heart. In this comprehensive review, we summarize the latest findings on the mechanisms and functions of m6A modification in organ fibrosis. By highlighting the potential of m6A modification as a therapeutic target, our goal is to encourage further research in this emerging field and support advancements in the clinical treatment of organ fibrosis.
Single-cell atlas of the pregnant equine endometrium before and after implantation
Authors: Jaworska Joanna , Tobolski Dawid , Salem Shebl E , Kahler Anne , Wocławek-Potocka Izabela , de Mestre Amanda M
Published: January 2025

Embryo implantation in the mare occurs just over one month after fertilization, coinciding with the production of chorionic gonadotropin. The factors that regulate this late implantation in the mare, and whether they are unique to horses or shared with more invasive embryo implantation in other species, remain poorly understood. This study aimed to determine and compare the transcriptome and subpopulations of endometrial cells before and after embryo implantation in the horse. Single-cell RNA sequencing was used to characterize the transcriptome of nearly 97,000 endometrial cells collected from biopsies of the endometrium at the beginning (day 33 of gestation) and after embryo implantation (day 42 of gestation) in mares. Sixteen immune and 24 non-immune cell clusters were identified, representing known major cell populations as well as novel subpopulations of horse immune cells such as resident innate lymphoid cells and mucosal-associated invariant T cells. Contrary to current knowledge, endometrial natural killer (eNK) cells were the most abundant endometrial leukocyte population during implantation in horses. Moreover, eNK cells not only expressed genes that may interact with fetal MHC I, such as LY49F, but also exert immunoregulatory functions independent of MHC I expression, such as CD96/TIGIT. Analogous to other species studied, upregulation of CXCR4 was found in several subpopulations of immune cells. Our results suggest that despite distinctive and later placentation compared with humans, horses share some key similarities in the mechanisms of embryo implantation.
Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts
Authors: Qiu Xiangyun
Published: January 2025

A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference. Specifically, motivated by the clustered distribution of known RNA structures, a collection of distinct DL models is trained by iteratively leaving one cluster out. Each DL model hence serves as an expert on all but one cluster in the training data. Consequently, for an ID sequence, all but one DL model makes accurate predictions consistent with one another, while an OOD sequence yields highly inconsistent predictions among all DL models. Through consensus analysis of DL predictions, test sequences are categorized as ID or OOD. ID sequences are subsequently predicted by averaging the DL models in consensus, and OOD sequences are predicted using physics-based models. Instead of remediating generalization gaps with alternative approaches such as transfer learning and sequence alignment, MoEFold2D circumvents unpredictable ID–OOD gaps and combines the strengths of DL and physics-based models to achieve accurate ID and robust OOD predictions.
Machine learning–based prediction of disease progression in primary progressive multiple sclerosis
Authors: Gurevich Michael , Zilkha-Falb Rina , Sherman Jia , Usdin Maxime , Raposo Catarina , Craveiro Licinio , Sonis Polina , Magalashvili David , Menascu Shay , Dolev Mark , Achiron Anat
Published: January 2025

Primary progressive multiple sclerosis (PPMS) affects 10–15% of multiple sclerosis patients and presents significant variability in the rate of disability progression. Identifying key biological features and patients at higher risk for fast progression is crucial to develop and optimize treatment strategies. Peripheral blood cell transcriptome has the potential to provide valuable information to predict patients’ outcomes. In this study, we utilized a machine learning framework applied to the baseline blood transcriptional profiles and brain MRI radiological enumerations to develop prognostic models. These models aim to identify PPMS patients likely to experience significant disease progression and who could benefit from early treatment intervention. RNA-sequence analysis was performed on total RNA extracted from peripheral blood mononuclear cells of PPMS patients in the placebo arm of the ORATORIO clinical trial (NCT01412333), using Illumina NovaSeq S2. Cross-validation algorithms from Partek Genome Suite (www.partek.com) were applied to predict disability progression and brain volume loss over 120 weeks. For disability progression prediction, we analysed blood RNA samples from 135 PPMS patients (61 females and 74 males) with a mean ± standard error age of 44.0 ± 0.7 years, disease duration of 5.9 ± 0.32 years and a median baseline Expanded Disability Status Scale (EDSS) score of 4.3 (range 3.5–6.5). Over the 120-week study, 39.3% (53/135) of patients reached the disability progression end-point, with an average EDSS score increase of 1.3 ± 0.16. For brain volume loss prediction, blood RNA samples from 94 PPMS patients (41 females and 53 males), mean ± standard error age of 43.7 ± 0.7 years and a median baseline EDSS of 4.0 (range 3.0–6.5) were used. Sixty-seven per cent (63/94) experienced significant brain volume loss. For the prediction of disability progression, we developed a two-level procedure. In the first level, a 10-gene predictor achieved a classification accuracy of 70.9 ± 4.5% in identifying patients reaching the disability end-point within 120 weeks. In the second level, a four-gene classifier distinguished between fast and slow disability progression with a 506-day cut-off, achieving 74.1 ± 5.2% accuracy. For brain volume loss prediction, a 12-gene classifier reached an accuracy of 70.2 ± 6.7%, which improved to 74.1 ± 5.2% when combined with baseline brain MRI measurements. In conclusion, our study demonstrates that blood transcriptome data, alone or combined with baseline brain MRI metrics, can effectively predict disability progression and brain volume loss in PPMS patients.
Relationship between sodium and diffusion MRI metrics in multiple sclerosis
Authors: Cipriano Emilio , Boffa Giacomo , Graziano Nicole , Wigley Claire , Petracca Maria , Schiavi Simona , Mancardi Giovanni Luigi , Klineova Sylvia , Boada Fernando , Lublin Fred , Inglese Matilde , Fleysher Lazar
Published: January 2025

Sodium MRI can measure in vivo sodium concentrations in people with multiple sclerosis, but the extent to which these alterations reflect metabolic dysfunction in the absence of tissue damage or neuroaxonal loss remains uncertain. Increases in total sodium concentration and extracellular sodium concentration are believed to be indicative of tissue disruption and extracellular space expansion. Conversely, increase in intracellular sodium concentration may represent early and transient responses to neuronal insult, preceding overt tissue damage. Here, we explored the relationship between total sodium concentration, intracellular sodium concentration and intracellular sodium volume fraction, which reflects extracellular sodium concentration, against histology-validated microstructural metrics obtained using diffusion MRI. Fifty-two individuals with multiple sclerosis and 26 healthy controls underwent 1H/23Na MRI. Microstructural parameters were derived using Diffusion Basis Spectrum Imaging and Neurite Orientation Dispersion and Density-Imaging models. A progressive worsening in total sodium concentration and intracellular sodium volume fraction was observed from HCs white matter to normal-appearing white matter and further into T2-hyperintense and T1-hypointense lesions. Both total sodium concentration and intracellular sodium volume fraction, but not intracellular sodium concentration, correlated with Neurite Orientation Dispersion and Density Imaging and Diffusion Basis Spectrum Imaging metrics within multiple sclerosis lesions. Our findings confirm the utility of total sodium concentration and extracellular sodium concentration as indicators of extracellular expansion and axonal loss and underscore intracellular sodium concentration as a valuable biomarker for metabolic dysfunction in multiple sclerosis.
Study on the resistance characteristics of corrugated flame arrester for hydrogen explosion
Authors: Cao Xingyan , Zhou Xiang , Zhou Jiyuan , Wang Zhenhua , Li Anqi , Bi Sheng , Wei Shanyang , Sun Shaochen , Ding Zhanwu
Published: January 2025

The resistance characteristics of a corrugated flame arrester in hydrogen explosions were investigated using a visual explosion-retardant system. By varying the structural parameters of the corrugated flame-retardant system and the concentration of hydrogen, the flame resistance characteristics and the mechanisms affecting hydrogen explosions were summarized. The results indicate that there was a clear correlation between the flame propagation and pressure rise during explosion resistance. When the flame resistance failed, a significant enhancement effect was observed at the backend of the flame-retardant system. Additionally, a noticeable oscillation occurred in the initial stage of flame propagation due to the obstruction caused by the flame-retardant system. The thickness and porosity of the flame-retardant system, along with the hydrogen concentration, influenced the intensities of shock waves and flames entering the narrow channel, as well as the physical and chemical effects during internal propagation, thereby affecting the overall flame resistance.