bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cr silicate as a prototype for engineering magnetic phases in air-stable two-dimensional transition-metal silicates
Authors: Doudin Nassar , Saritas Kayahan , Zheng Jin-Cheng , Boscoboinik J Anibal , Sadowski Jerzy T , Shafer Padraic , N’Diaye Alpha T , Li Min , Ismail-Beigi Sohrab , Altman Eric I 
Published: August 2023

Abstract
Identifying environmentally inert, ferromagnetic two-dimensional (2D) materials with high Curie temperatures (T
c) down to the single layer limit has been an obstacle to fundamental studies of 2D magnetism and application of 2D heterostructures to spin-polarized devices. To address this challenge, the growth, structure and magnetic properties of a 2D Cr-silicate single layer on Pt(111) was investigated experimentally and theoretically. The layer was grown by sequentially depositing SiO and Cr followed by annealing in O2. Scanning tunneling microscopy (STM), low-energy electron diffraction (LEED), and low energy electron microscopy all indicated a well-ordered layer that uniformly covered the surface, with STM and LEED indicating that the silicate relaxed to its favored lattice constant. Further experimental characterizations demonstrated that the Cr was nominally 3+ but with a lower electron density than typical trivalent Cr compounds. Comparison with theory identified a Cr2Si2O9 structure that resembles a single layer of a dehydrogenated dioctahedral silicate. Magnetic circular dichroism in x-ray absorption spectroscopy revealed a ferromagnetically ordered state up to at least 80 K. Theoretical analysis revealed that the Cr in a dehydrogenated Cr-silicate/Pt(111) is more oxidized than Cr in freestanding Cr2Si2O9H4 layers. This greater oxidation was found to enhance ferromagnetic coupling and suggests that the magnetism may be tuned by doping. The 2D Cr-silicate is the first member of a broad series of possible layered first-row transition metal silicates with magnetic order; thus, this paper introduces a new platform for investigating 2D ferromagnetism and the development of magnetoelectronic and spintronic devices by stacking 2D atomic layers.
Simulation study of the tunnel junction position effect on the parameters of the InxGa1-xN dual junction solar cell
Authors: Aoulmit Salim , Bekhouche Khaled , Kaghouche Bessem , Guentri Hocine 
Published: August 2023

Abstract
The present work presents a SILVACO-Atlas numerical simulation to investigate the effect of the tunnel junction position on the performance of InxGa1−xN double-junction solar cells under AM1.5 solar illumination. The proposed cell is composed of two PN sub-cells, an upper sub-cell in In0.1Ga0.9N and a lower sub-cell in In0.4Ga0.6N for the p-type and In0.2Ga0.8N for the n-type, connected by a tunnel junction in In0.4Ga0.6N. The cell offers a remarkable open-circuit voltage value of about 3.9 V and a good fill shape value of about 93. A cell with a small overall thickness can offer better transfer efficiency than a cell with a large thickness if the tunnel junction position is carefully chosen. The proposed cell can achieve a transfer efficiency of around 18% with an overall thickness of 0.652 μm.
Data efficiency and extrapolation trends in neural network interatomic potentials
Authors: Vita Joshua A , Schwalbe-Koda Daniel 
Published: August 2023

Abstract
Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs.
Physics-informed neural networks for modeling astrophysical shocks
Authors: Moschou S P , Hicks E , Parekh R Y , Mathew D , Majumdar S , Vlahakis N 
Published: August 2023

Abstract
Physics-informed neural networks (PINNs) are machine learning models that integrate data-based learning with partial differential equations (PDEs). In this work, for the first time we extend PINNs to model the numerically challenging case of astrophysical shock waves in the presence of a stellar gravitational field. Notably, PINNs suffer from competing losses during gradient descent that can lead to poor performance especially in physical setups involving multiple scales, which is the case for shocks in the gravitationally stratified solar atmosphere. We applied PINNs in three different setups ranging from modeling astrophysical shocks in cases with no or little data to data-intensive cases. Namely, we used PINNs (a) to determine the effective polytropic index controlling the heating mechanism of the space plasma within 1% error, (b) to quantitatively show that data assimilation is seamless in PINNs and small amounts of data can significantly increase the model’s accuracy, and (c) to solve the forward time-dependent problem for different temporal horizons. We addressed the poor performance of PINNs through an effective normalization approach by reformulating the fluid dynamics PDE system to absorb the gravity-caused variability. This led to a huge improvement in the overall model performance with the density accuracy improving between 2 and 16 times. Finally, we present a detailed critique on the strengths and drawbacks of PINNs in tackling realistic physical problems in astrophysics and conclude that PINNs can be a powerful complimentary modeling approach to classical fluid dynamics solvers.
A study of transfer learning in digital rock properties measurement
Authors: Haq M I K , Yulita I N , Dharmawan I A 
Published: August 2023

Abstract
The measurement of physical parameters of porous rock, which constitute reservoirs, is an essential part of hydrocarbon exploration. Typically, the measurement of these physical parameters is carried out through core analysis in a laboratory, which requires considerable time and high costs. Another approach involves using digital rock models, where the physical parameters are calculated through image processing and numerical simulations. However, this method also requires a significant amount of time for estimating the physical parameters of each rock sample. Machine learning, specifically convolutional neural network (CNN) algorithms, has been developed as an alternative method for estimating the physical parameters of porous rock in a shorter time frame. The advancement of CNN, particularly through transfer learning using pre-trained models, has contributed to rapid prediction capabilities. However, not all pre-trained models are suitable for estimating the physical parameters of porous rock. In this study, transfer learning was applied to estimate parameters of sandstones such as porosity, specific surface area, average grain size, average coordination number, and average throat radius. Six types of pre-trained models were utilized: ResNet152, DenseNet201, Xception, InceptionV3, InceptionResNetV2, and MobileNetV2. The results of this study indicate that the DenseNet201 model achieved the best performance with an error rate of 2.11%. Overall, this study highlights the potential of transfer learning to ultimately lead to more efficient and effective computation.
Axonal transport of Frizzled5 by Alcadein α-containing vesicles is associated with kinesin-1
Authors: Shiraki Yuzuha , Mitsuma Monet , Takada Ritsuko , Hata Saori , Kitamura Akira , Takada Shinji , Kinjo Masataka , Taru Hidenori , Müller Ulrike C. , Yamamoto Tohru , Sobu Yuriko , Suzuki Toshiharu 
Published: August 2023

Alcadein α (Alcα) and amyloid-β protein precursor (APP) are cargo receptors that associate vesicles with kinesin-1. These vesicles, which contain either Alcα or APP, transport various proteins/cargo molecules into axon nerve terminals. Here, we analyzed immune-isolated Alcα- and APP-containing vesicles of adult mouse brains with LC–MS/MS and identified proteins present in vesicles that contained either Alcα or APP. Among these proteins, Frizzled-5 (Fzd5), a Wnt receptor, was detected mainly in Alcα vesicles. Although colocalization ratios of Fzd5 with Alcα are low in the neurites of differentiating neurons by a low expression of Fzd5 in embryonic brains, the suppression of Alcα expression decreased the localization of Fzd5 in neurites of primary cultured neurons. Furthermore, Fzd5-EGFP expressed in primary cultured neurons was preferentially transported in axons with the transport velocities of Alcα vesicles. In synaptosomal fractions of adult-mice brains that express higher levels of Fzd5, the amount of Fzd5 and the phosphorylation level of calcium/calmodulin-dependent protein kinase-II were reduced in the Alcα-deficient mice. These results suggest that reduced transport of Fzd5 by Alcα-containing vesicles associated with kinesin-1 in axon terminals may impair the response to Wnt ligands in the noncanonical Ca2+-dependent signal transduction pathway at nerve terminals of mature neurons.
Beyond Diagnosis: Representing the Hotline in The Slender Thread
Authors: Zeavin Hannah 
Published: August 2023

Abstract
Revisiting the little-noted 1965 film, The Slender Thread, this article argues that its representation of the suicide hotline constellates midcentury anxieties concerning psychiatric care, race, and gender. The Slender Thread provides a story of the suicide hotline at an “early crossroads” in the history of the suicide hotline as it “turned . . . toward cure and the carceral while gloving that power within the protocol of this radical care.”
This is the Poitier Effect: Black fury is and must be sublated into Black care to bypass revolutionary action and insurrection.
Diagnosing America: The Literatures of Mental Health in the US
Authors: Mendelman Lisa , Hutner Gordon 
Published: August 2023

Abstract
Once one is alive to it, scholarship on the literatures of mental health in the US seems to be everywhere, though it is evolving out of so many different sources that its parameters as a subfield can be hard to establish. That is, many studies not overtly focused on psychology and well-being investigate these concerns—from studies of national political mindsets to gendered, racialized, and otherwise biopolitically defined life experiences. The ever-widening scope of the health humanities can also be seen either to include an expanding ring of mental health studies or, in a post-Cartesian world, to be nearly synonymous with studies of psychic life. The scholarly turns to neuroscience, affect, and ontology express a similar set of interests, even as they align with a broader cultural attention to the so-called mental health epidemic that the COVID-19 pandemic, climate disasters, race-based violence, and our myriad other global health crises exacerbate.In part, our present interest reflects what many might consider as an overdue reckoning with the literary representations of the inequitable psychological vulnerabilities of US existence.
Comics and American Cultural Policy
Authors: Beaty Bart 
Published: August 2023

Abstract
This review considers the contributions of two 2021 monographs—Christopher J. Gilbert’s Caricature and National Character: The United States at War and Paul Hirsch’s Pulp Empire: A Secret History of Comic Book Imperialism—each of which examines the role of cartooning in shaping attitudes towards warfare, imperialism, and nationalism in the US in the twentieth century.The highly repetitive and remarkably codified nature of the comic-book story is . . . the thing that made it attractive for propagandistic purposes.
Pollination mechanism inSerapiaswith no pollinaria reconfiguration
Authors: Lanzino Micaela , Palermo Anna Maria , Pellegrino Giuseppe 
Published: August 2023

AbstractOrchidaceae, one of the most numerous families in the world’s flora, have evolved various pollination strategies to favour cross-pollination, such as deceptive pollination and pollinarium reconfiguration. Among the terrestrial orchids of the Mediterranean, only species belonging to the genus Serapias show a strategy defined as shelter imitation. The floral elements form a tubular structure that insects use during their resting phases. The purpose of this article was to clarify the mechanisms that guarantee pollination with particular attention to the morphological interactions between orchids and pollinators and whether pollinaria reconfiguration is necessary in the promotion of cross-pollination in Serapias. Breeding system experiments and hand-pollination treatments indicated that Serapias was highly self-compatible, shows low value of natural fruit set and is pollinator limited. Time-lapse photos showed that the pollinarium had no refolding of the stipe or caudicle after its removal from the flower. The morphology of the flower determined the attack of the pollinarium on the occiput/vertex of insect. When the insect left the flower, the pollinarium was unable to encounter the stigma. When the insect made a second visit to another flower, the pollen masses of the first pollinarium ended up on the stigma and at the same time, the insect picked up a second pollinarium. Our observations and analyses suggested that morphological interactions between flower and pollinator are crucial to the success of pollination and to prevent self-pollination and thus that pollinarium reconfiguration is unnecessary in shelter deceptive orchids, such as Serapias species, for the promotion of cross-pollination. Serapias represent a case of interactions between plant and pollinator; the formation of the tubular shape of the flower is an essential preadaptation for the development of resting site mimicry originating exclusively in Serapias among Mediterranean orchids.
Adjusting for gene-specific covariates to improve RNA-seq analysis
Authors: Jeon Hyeongseon , Lim Kyu-Sang , Nguyen Yet , Nettleton Dan 
Published: August 2023

AbstractSummaryThis article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey’s q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method’s premise that the null probability varies with a gene-specific covariate variable.Availability and implementationThe source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.
AARDVARK: an automated reversion detector for variants affecting resistance kinetics
Authors: Moreno Thaidy , Magana Joaquin , Quigley David A 
Published: August 2023

Abstract

Summary
Resistance to two classes of FDA-approved therapies that target DNA repair-deficient tumors is caused by mutations that restore the tumor cell's DNA repair function. Identifying these “reversion” mutations currently requires manual annotation of patient tumor sequence data. Here we present AARDVARK, an R package that automatically identifies reversion mutations from DNA sequence data.

Availability and implementation
AARDVARK is implemented in R (≥3.5). It is available on GitHub at https://github.com/davidquigley/aardvark. It is licensed under the MIT license.
Correlations among chromosome numbers, nuclear DNA contents and climatic variables of South American Cactaceae in a phylogenetic context
Authors: Las Peñas María Laura , Bauk Karen , Rossi , Nicola , Bernardello Gabriel 
Published: August 2023

Abstract
Cactaceae are American members of arid and semi-arid regions and are also present in tropical forests and temperate climates. The taxonomy is complex owing to large morphological variability, and polyploidy is a significant mechanism in cactus speciation associated with geographical and environmental range expansions. In South America, Cactaceae have a wide distributional range and provide an informative model in which to examine the correlation between ploidy levels and genome sizes with geographical and bioclimatic variables. We tested whether ploidy levels and DNA contents are related to elevation and the 19 WorldClim variables, and we mapped chromosome numbers and amounts of DNA on a molecular phylogeny to interpret their evolution. We performed Pearson correlation tests between the response variables and each climatic variable and used a multivariate analysis to assess the pattern of variation among variables and chromosome numbers. Our data suggest that polyploidy had independent origins within each subfamily. Chromosome numbers were related to variables associated with temperature, whereas DNA contents were related mostly to precipitation. A negative correlation was observed between the amount of DNA in one chromosome set and the increase in the ploidy level. Based on the results, the small genome can be considered ecologically important as an adaptation to higher temperatures and droughts.
Assessing blood oxygen level–dependent signal variability as a biomarker of brain injury in sport-related concussion
Authors: Anderson Evan D , Talukdar Tanveer , Goodwin Grace , Di Pietro Valentina , Yakoub Kamal M , Zwilling Christopher E , Davies David , Belli Antonio , Barbey Aron K 
Published: August 2023

AbstractMild traumatic brain injury is a complex neurological disorder of significant concern among athletes who play contact sports. Athletes who sustain sport-related concussion typically undergo physical examination and neurocognitive evaluation to determine injury severity and return-to-play status. However, traumatic disruption to neurometabolic processes can occur with minimal detectable anatomic pathology or neurocognitive alteration, increasing the risk that athletes may be cleared for return-to-play during a vulnerable period and receive a repetitive injury. This underscores the need for sensitive functional neuroimaging methods to detect altered cerebral physiology in concussed athletes. The present study compared the efficacy of Immediate Post-concussion Assessment and Cognitive Testing composite scores and whole-brain measures of blood oxygen level–dependent signal variability for classifying concussion status and predicting concussion symptomatology in healthy, concussed and repetitively concussed athletes, assessing blood oxygen level–dependent signal variability as a potential diagnostic tool for characterizing functional alterations to cerebral physiology and assisting in the detection of sport-related concussion. We observed significant differences in regional blood oxygen level–dependent signal variability measures for concussed athletes but did not observe significant differences in Immediate Post-concussion Assessment and Cognitive Testing scores of concussed athletes. We further demonstrate that incorporating measures of functional brain alteration alongside Immediate Post-concussion Assessment and Cognitive Testing scores enhances the sensitivity and specificity of supervised random forest machine learning methods when classifying and predicting concussion status and post-concussion symptoms, suggesting that alterations to cerebrovascular status characterize unique variance that may aid in the detection of sport-related concussion and repetitive mild traumatic brain injury. These results indicate that altered blood oxygen level–dependent variability holds promise as a novel neurobiological marker for detecting alterations in cerebral perfusion and neuronal functioning in sport-related concussion, motivating future research to establish and validate clinical assessment protocols that can incorporate advanced neuroimaging methods to characterize altered cerebral physiology following mild traumatic brain injury.
NDN-RBE: An Accountable Privacy Aware Access Control Framework For NDN
Authors: Sultan Nazatul Haque , Varadharajan Vijay , Dulal Saurab , Camtepe Seyit , Nepal Surya 
Published: August 2023

Abstract
Named Data Networking (NDN) is an emerging network architecture. An important characteristic of NDN is its in-network cache, which enables Data packets to be available from multiple locations on the Internet. Hence the enforcement of access control mechanisms becomes even more critical in the NDN. This paper proposes a novel access control scheme referred to as Role-Based Encryption for NDN (NDN-RBE), which uses a broadcast encryption mechanism to achieve secure data access control. Our scheme uses the role inheritance property of the traditional Role-Based Access Control (RBAC) model to achieve efficient data access control over hierarchical content. This makes our scheme particularly suitable for large-scale real-world content-centric services like Netflix. Our scheme also supports additional design features such as anonymous signature-based authentication, batch signature verification and two types of privilege revocations. In addition, our formal security analysis demonstrates that our scheme is provably secure against Chosen Plaintext Attacks. Our performance and functionality comparison show that our scheme outperforms other notable existing works in terms of security, functionality, computation, communication and storage overhead. Furthermore, our experimental results show an improvement in content delivery time of the order of 15 percent compared with the other closely related works.
Signed Rhyme and Rhythm With Deaf Children: Early Childhood Teacher Interviews
Authors: Holcomb Leala , Higgins Michael 
Published: August 2023

Abstract
Early childhood programs promote language play opportunities due to the well-documented positive influences on cognition, language, and literacy development. This qualitative investigation explores language play through the form of signed rhyme and rhythm among young deaf children. Teachers specializing in deaf education within an early childhood program were interviewed to assess their knowledge and implementation of signed rhyme and rhythm in their pedagogical practices. The results reveal that despite recognizing numerous advantages and deeming signed rhyme and rhythm as essential for deaf children’s development, these teachers perceive themselves as lacking adequate knowledge and preparation to effectively incorporate this approach in their classrooms. To address this gap, professional development opportunities should be provided to develop knowledge and confidence in teachers, empowering them to effectively implement signed rhyme and rhythm with deaf children.