bluuebunny/crossref_metadata_2025 huggingface dataset:
Rearrangement of cell types in the rat carotid body neurogenic niche induced by chronic intermittent hypoxia
Authors: Caballero‐Eraso Candela , Colinas Olaia , Sobrino Verónica , González‐Montelongo Rafaela , Cabeza José María , Gao Lin , Pardal Ricardo , López‐Barneo José , Ortega‐Sáenz Patricia
Published: January 2023

The carotid body (CB) is a prototypical acute oxygen (O2)‐sensing organ that mediates reflex hyperventilation and increased cardiac output in response to hypoxaemia. CB overactivation, secondary to the repeated stimulation produced by the recurrent episodes of intermittent hypoxia, is believed to contribute to the pathogenesis of sympathetic hyperactivity present in sleep apnoea patients. Although CB functional plasticity induced by chronic intermittent hypoxia (CIH) has been demonstrated, the underlying mechanisms are not fully elucidated. Here, we show that CIH induces a small increase in CB volume and rearrangement of cell types in the CB, characterized by a mobilization of immature quiescent neuroblasts, which enter a process of differentiation into mature, O2‐sensing and neuron‐like, chemoreceptor glomus cells. Prospective isolation of individual cell classes has allowed us to show that maturation of CB neuroblasts is paralleled by an upregulation in the expression of specific glomus cell genes involved in acute O2‐sensing. CIH enhances mitochondrial responsiveness to hypoxia in maturing neuroblasts as well as in glomus cells. These data provide novel perspectives on the pathogenesis of CB‐mediated sympathetic overflow that may lead to the development of new pharmacological strategies of potential applicability in sleep apnoea patients.

image

Key points

Obstructive sleep apnoea is a frequent condition in the human population that predisposes to severe cardiovascular and metabolic alterations.

Activation of the carotid body, the main arterial oxygen‐sensing chemoreceptor, by repeated episodes of hypoxaemia induces exacerbation of the carotid body‐mediated chemoreflex and contributes to sympathetic overflow characteristic of sleep apnoea patients.

In rats, chronic intermittent hypoxaemia induces fast neurogenesis in the carotid body with rapid activation of neuroblasts, which enter a process of proliferation and maturation into O2‐sensing chemoreceptor glomus cells.

Maturing carotid body neuroblasts and glomus cells exposed to chronic intermittent hypoxia upregulate genes involved in acute O2 sensing and enhance mitochondrial responsiveness to hypoxia.

These findings provide novel perspectives on the pathogenesis of carotid body‐mediated sympathetic hyperactivation. Pharmacological modulation of carotid body fast neurogenesis could help to ameliorate the deleterious effects of chronic intermittent hypoxaemia in sleep apnoea patients.
Hybrid Cost-Tolerance Allocation and Production Strategy Selection for Complex Mechanisms: Simulation and Surrogate Built-In Optimization Models
Authors: Khezri Amirhossein , Homri Lazhar , Etienne Alain , Dantan Jean-Yves
Published: January 2023

In manufacturing companies, assembly is an essential process to obtain the final product. The life cycle of an assembly product depends on various production strategies, e.g., resource allocation, rework decision, selection strategy, etc. In this regard, achieving a reliable assembly product commence with engineering a comprehensive design plan which can mitigate various uncertainties a company can face. The counteraction of uncertainties can be altered by introducing a set of tolerances into the design of the components. Tolerances define a practical margin on components design without downgrading the required performance of products. Thus, producers are confronted with high-quality requirements, cost pressure, and a rising number of demands. On these bases, this paper aims at modeling a statistical framework for a set of production strategies, including resource allocation (as a decision to assign practical resources to components) and reworking decision (as a decision to improve components’ conformity rate). Moreover, a generic simulation and surrogate approach are established to evaluate the performance of the assembled product. Within this approach, simulation and surrogate models can be used to investigate a variety of deviations over components’ geometries within the process deviation domain and deploy reworking decision. Ultimately, a modular costing system is developed, and a genetic algorithm is adapted to locate optimal solutions. In addition, the applicability of the statistical model is studied on an assembly product.
Expert Views on Evidence-Based Classification for Goalball: A Delphi Study
Authors: Martin Anna M. , Ryu Donghyun , Jackson Robin C. , Mann David L.
Published: January 2023

Para sport classification aims to minimize the impact of impairments on the outcome of competition. The International Paralympic Committee requires classification systems to be evidence based and sport specific, yet the sport of goalball uses a structure that is not supported by evidence demonstrating its legitimacy for competition. This study aimed to establish expert opinions on how a sport-specific system of classification should be structured in the sport of goalball. Using a three-round Delphi survey, 30 international experts expressed their views across topics linked to goalball classification. Participants were divided as to whether the current system fulfills the aim to minimize the impact of impairment on competition. Most felt that less impairment should be required to compete but that the one-class structure should remain. Experts identified measures of visual function that should be considered and 15 core components of individual goalball performance. Findings constitute a crucial first step toward evidence-based classification in goalball.
A Segmentation Analysis of American Sports Bettors by Involvement
Authors: Dwyer Brendan , Shapiro Stephen L. , Drayer Joris
Published: January 2023

Sports betting in the United States is exploding in popularity and has the potential to change the way sports fans interact with sports properties and sports content. However, not all sports bettors are the same, and market segmentation research provides a resource for more targeted communication and marketing strategies. Utilizing behavioral and psychographic data, the current study segmented 1,077 sports bettors by involvement. The segments were then contrasted on a number of factors within the framework of Mowen’s 3M model of motivation and personality. A sample of 513 nonbetting sports fans was also included as a segment within the analyses. Statistically significant differences were found at the motivational, elemental, compound, and surface trait levels between the betting segments and between the betting and the nonbetting sports fans. The findings point to a strong emotional draw regardless of involvement yet a clear need for the betting industry to educate on issues related to jurisdictional legality and common language.
Impact of Vancomycin Loading Doses and Dose Escalation on Glomerular Function and Kidney Injury Biomarkers in a Translational Rat Model
Authors: Chang Jack , Pais Gwendolyn M. , Engel Patti L. , Klimek Patryk , Marianski Sylwia , Valdez Kimberly , Scheetz Marc H.
Published: January 2023

Vancomycin-induced kidney injury is common, and outcomes in humans are well predicted by animal models. This study employed our translational rat model to investigate temporal changes in the glomerular filtration rate (GFR) and correlations with kidney injury biomarkers related to various vancomycin dosing strategies.
Selenomonas sputigena Interactions with Gingival Epithelial Cells That Promote Inflammation
Authors: Hawkes Colin G. , Hinson Annie N. , Vashishta Aruna , Read Curtis B. , Carlyon Jason A. , Lamont Richard J. , Uriarte Silvia M. , Miller Daniel P.
Published: January 2023

Increased prevalence and abundance of
Selenomonas sputigena
have been associated with periodontitis, a chronic inflammatory disease of tooth-supporting tissues, for more than 50 years. Over the past decade, molecular surveys of periodontal disease using 16S and shotgun metagenomic sequencing approaches have confirmed the disease association of classically recognized periodontal pathogens such as
Porphyromonas gingivalis
,
Treponema denticola
, and
Tannerella forsythia
while highlighting previously underappreciated organisms such as
Filifactor alocis
and
S. sputigena
.
Wolbachia
Promotes Its Own Uptake by Host Cells
Authors: Nevalainen Lindsay B. , Layton Emily M. , Newton Irene L. G.
Published: January 2023

Wolbachia pipientis
is an incredibly widespread bacterial symbiont of insects, present in an estimated 25 to 52% of species worldwide.
Wolbachia
is faithfully maternally transmitted both in a laboratory setting and in the wild. In an established infection,
Wolbachia
is primarily intracellular, residing within host-derived vacuoles that are associated with the endoplasmic reticulum.
Escherichia coli-Derived Outer Membrane Vesicles Relay Inflammatory Responses to Macrophage-Derived Exosomes
Authors: Imamiya Risa , Shinohara Akari , Yakura Daisuke , Yamaguchi Takehiro , Ueda Koji , Oguro Ami , Minamiyama Yukiko , Ichikawa Hiroshi , Horiguchi Yasuhiko , Osada-Oka Mayuko
Published: January 2023

Recently, extracellular membrane vesicles (EVs) were regarded as drivers that carry cargo such as proteins, lipids, metabolites, RNA, and DNA for intracellular signaling transduction. Mammalian cells release various types of EVs, including microvesicles shed from the plasma membrane, exosomes from endosomes, apoptotic bodies, and others.
Longitudinal Study of Dynamic Epidemiology of Extended-Spectrum Beta-Lactamase-Producing Escherichia coli in Pigs and Humans Living and/or Working on Pig Farms
Authors: Dohmen Wietske , Liakopoulos Apostolos , Bonten Marc J. M. , Mevius Dik J. , Heederik Dick J. J.
Published: January 2023

Extended-spectrum beta-lactamase (ESBL)-producing
Escherichia coli
represents a public health hazard due to reduced therapeutic options for the treatment of infections. Although direct contact with pigs is considered a risk factor for human ESBL-producing
E. coli
carriage through occupational exposure, nationwide data regarding the occurrence of such isolates among pigs and humans living and/or working on farms remain scarce.
The subhorizon Ofh as a prospective geoindicator of temperate soil quality in relation to selected trace elements and PAHs: a review
Authors: Dołęgowska Sabina , Sołtys Agnieszka , Krzciuk Karina , Wideł Dariusz
Published: January 2023

Soils, next to vegetation, are one of the most popular indicators in environmental quality studies. The response of soil to air pollution is less complex than the response of vegetation. In soils, mainly in their upper layers, the deposition of compounds may occur almost undisturbed; therefore, results of chemical analyses are readily used in the assessment of changes caused by natural processes and anthropogenic activities. In a temperate climate zone, the outermost layer of forest soils is formed by an organic horizon (O), which comprises two basic subhorizons, of which the organic fermentative-humic subhorizon (Ofh) is recognized as the most noteworthy. The subhorizon Ofh encompasses partially and fully decomposed organic matter (humic and fulvic acids, humins), as well as Fe, Mn, and Al oxides and hydroxides, forming a characteristic tangled mat. The structure pattern of this subhorizon is responsible for the higher concentrations of elements and organic compounds, which are immobilized through sorption, complexation, or precipitation. Some previous studies have shown that the subhorizon Ofh has enhanced accumulative properties in comparison to the remaining subjacent soil horizons. Results derived from soil studies in the Holy Cross Mountains (south-central Poland) have also revealed that this subhorizon is mainly enriched in polycyclic aromatic compounds (polycyclic aromatic hydrocarbons) and some trace elements (Hg, Cd, Cu, Pb, and Zn). This could make the subhorizon Ofh a potential geoindicator of soil quality in relation to these compounds over the temperate climate zone. However, further study on this issue is needed to confirm its application in environmental monitoring.
Dynamical systems analysis in $$f(T,\phi )$$ gravity
Authors: Duchaniya L. K. , Kadam S. A. , Said Jackson Levi , Mishra B.
Published: January 2023

Teleparallel based cosmological models provide a description of gravity in which torsion is the mediator of gravitation. Several extensions have been made within the so-called teleparallel equivalent of general relativity which is equivalent to general relativity at the level of the equations of motion where attempts are made to study the extensions of this form of gravity and to describe more general functions of the torsion scalar T. One of these extensions is $$f(T,\phi )$$

f
(
T
,
ϕ
)

gravity; T and $$\phi $$
ϕ
respectively denote the torsion scalar and scalar field. In this work, the dynamical system analysis has been performed for this class of theories to obtain the cosmological behaviour of a number of models. Two models are presented here with some functional form of the torsion scalar and the critical points are obtained. For each critical point, the stability behaviour and the corresponding cosmology are shown. Through the graphical representation, the equation of state parameter and the density parameters for matter-dominated, radiation-dominated and dark energy phase are also presented for both the models.
Heavy quark potential and LQCD based quark condensate at finite magnetic field
Authors: Nilima Indrani , Bandyopadhyay Aritra , Ghosh Ritesh , Ghosh Sabyasachi
Published: January 2023

In the present work, we have studied heavy quarkonia potential in hot and magnetized quark gluon plasma. Inverse magnetic catalysis (IMC) effect is incorporated within the system through the magnetic field modified Debye mass by modifying the effective quark masses. We have obtained the real and imaginary part of the heavy quark potential in this new scenario. After the evaluation of the binding energy and the decay width we comment about the dissociation temperatures of the heavy quarkonia in presence of magnetic field.
Learning from Highly Imbalanced Big Data with Label Noise
Authors: Johnson Justin M. , Kennedy Robert K. L. , Khoshgoftaar Taghi M.
Published: January 2023

This study explores the effects of class label noise on detecting fraud within three highly imbalanced healthcare fraud data sets containing millions of claims and minority class sizes as small as 0.1%. For each data set, 29 noise distributions are simulated by varying the level of class noise and the distribution of noise between the fraudulent and non-fraudulent classes. Four popular machine learning algorithms are evaluated on each noise distribution using six rounds of five-fold cross-validation. Performance is measured using the area under the precision-recall curve (AUPRC), true positive rate (TPR), and true negative rate (TNR) in order to understand the effect of the noise level, noise distribution, and their interactions. AUPRC results show that negative class noise, i.e. fraudulent samples incorrectly labeled as non-fraudulent, is the most detrimental to model performance. TPR and TNR results show that there are significant trade-offs in class-wise performance as noise transitions between the positive and the negative class. Finally, results reveal how overfitting negatively impacts the classification performance of some learners, and how simple regularization can be used to combat this overfitting and improve classification performance across all noise distributions.
Incorporating Normalized L1 Penalty and Eigenvalue Constraint for Causal Structure Learning
Authors: Wang Yunfeng , Zhu Yuelong , Hang Tingting , Lu Jiamin , Feng Jun
Published: January 2023

Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation matrix. Following it are nonlinear variants that can uncover nonlinear causal relationships. However, the nonlinear variant which considers the ℓ1 penalty as part of its optimization objective may not effectively eliminate false predictions. In this paper, we investigate the defect of the model that the ℓ1 penalty cannot effectively make the relation matrix sparse, thus introduces false predictions. Besides, the acyclicity constraint is unable to identify large circles within the margin of identification error, thus is unable to guarantee acyclicity of inferred causal relationships. Based on the theoretical and empirical analysis of the defects, we propose the normalized ℓ1 penalty which replaces the original ℓ1 penalty with a normalized first-order matrix norm, and propose a constraint based on eigenvalue to substitute the original acyclicity constraint. We then compare our proposed model NEC with three models to show considerable performance improvement. We further conduct experiments to show the effectiveness of the normalized ℓ1 penalty and the eigenvalue constraint.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Authors: Chen Huiming , Wang Huandong , Yao Quanming , Li Yong , Jin Depeng , Yang Qiang
Published: January 2023

Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The primary concerns in FedOpt can be attributed to the model divergence and communication efficiency, which significantly affect the performance. In this article, we propose a new method, i.e., LoSAC, to learn from heterogeneous distributed data more efficiently. Its key algorithmic insight is to locally update the estimate for the global full gradient after each regular local model update. Thus, LoSAC can keep clients’ information refreshed in a more compact way. In particular, we have studied the convergence result for LoSAC. Besides, the bonus of LoSAC is the ability to defend the information leakage from the recent technique Deep Leakage Gradients (DLG). Finally, experiments have verified the superiority of LoSAC comparing with state-of-the-art FedOpt algorithms. Specifically, LoSAC significantly improves communication efficiency by more than 100% on average, mitigates the model divergence problem, and equips with the defense ability against DLG.
A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval
Authors: Wu Zongda , Xie Jian , Shen Shigen , Lin Chongze , Xu Guandong , Chen Enhong
Published: January 2023

In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.
DDIFN: A Dual-discriminator Multi-modal Medical Image Fusion Network
Authors: Liu Hui , Li Shanshan , Zhu Jicheng , Deng Kai , Liu Meng , Nie Liqiang
Published: January 2023

Multi-modal medical image fusion is a long-standing important research topic that can obtain informative medical images and assist doctors diagnose and treat diseases more efficiently. However, most fusion methods extract and fuse features by subjectively defining constraints, which easily distorts the unique information of source images. In this work, we present a novel end-to-end unsupervised network to fuse multi-modal medical images. It is composed of a generator and two symmetrical discriminators. The former aims to generate a ”real-like” fused image based on a specifically designed content and structure loss, while the latter are devoted to distinguishing the differences between the fused image and the source ones. They are trained alternately until discriminators cannot distinguish the fused image from the source ones. In addition, the symmetrical discriminator scheme is conducive to maintaining the feature consistency among different modalities. More importantly, to enhance the retention degree of texture details, U-Net is adopted as the generator heuristically, where the up-sampling method is modified to bilinear interpolation for avoiding checkerboard artifacts. As for the optimization, we define the content loss function, which preserves the gradient information and pixel activity of source images. Both visual analysis and quantitative evaluation of experimental results show the superiority of our method as compared to the cutting-edge baselines.
High-Level Approaches to Hardware Security: A Tutorial
Authors: Pearce Hammond , Karri Ramesh , Tan Benjamin
Published: January 2023

Designers use third-party
intellectual property (IP)
cores and outsource various steps in the
integrated circuit (IC)
design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC and pirate the IP. Similarly, if attackers get hold of a design, they can insert malicious circuits or take advantage of “backdoors” in a design. Unintended design bugs can also result in security weaknesses. This tutorial paper provides an introduction to the domain of hardware security through two pedagogical examples of hardware security problems. The first is a walk-through of the scan chain-based side channel attack. The second is a walk-through of logic locking of digital designs. The tutorial material is accompanied by open access digital resources that are linked in this article.
Satisfiability Modulo Ordering Consistency Theory for SC, TSO, and PSO Memory Models
Authors: Fan Hongyu , Sun Zhihang , He Fei
Published: January 2023

Automatically verifying multi-threaded programs is difficult because of the vast number of thread interleavings, a problem aggravated by weak memory consistency. Partial orders can help with verification because they can represent many thread interleavings concisely. However, there is no dedicated decision procedure for solving partial-order constraints.

In this article, we propose a novel
ordering consistency theory
for concurrent program verification that is applicable not only under sequential consistency, but also under the TSO and PSO weak memory models. We further develop an efficient theory solver, which checks consistency incrementally, generates minimal conflict clauses, and includes a custom propagation procedure. We have implemented our approach in a tool, called
Zord
, and have conducted extensive experiments on the
SV-COMP 2020 ConcurrencySafety
benchmarks. Our experimental results show a significant improvement over the state-of-the-art.
Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics
Authors: Alsoliman Anas , Rigoni Giulio , Callegaro Davide , Levorato Marco , Pinotti Cristina M. , Conti Mauro
Published: January 2023

Cheap
commercial off-the-shelf (COTS)
First-Person View (FPV)
drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost attack opportunities to malicious users. Thus, effective methods to detect the presence of unknown and non-cooperating drones within a restricted area are highly demanded. Approaches based on detection of drones based on emitted video stream have been proposed, but were not yet shown to work against other similar benign traffic, such as that generated by wireless security cameras. Most importantly, these approaches were not studied in the context of detecting new unprofiled drone types. In this work, we propose a novel drone detection framework, which leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (we call pivots), which we use as features for a machine learning classifier. We show that our framework can achieve up to 99% in detection accuracy over an encrypted WiFi channel using only 170 packets originated from the drone within 820ms time period. Our framework is able to identify drone transmissions even among very similar WiFi transmissions (such as video streams originated from security cameras) as well as in noisy scenarios with background traffic. Furthermore, the design of our pivot features enables the classifier to detect unprofiled drones in which the classifier has never trained on and is refined using a novel feature selection strategy that selects the features that have the discriminative power of detecting new unprofiled drones.
AQuaMoHo
: Localized Low-cost Outdoor Air Quality Sensing over a Thermo-hygrometer
Authors: Pramanik Prithviraj , Karmakar Prasenjit , Sharma Praveen Kumar , Chatterjee Soumyajit , Roy Abhijit , Mandal Santanu , Nandi Subrata , Chakraborty Sandip , Saha Mousumi , Saha Sujoy
Published: January 2023

Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named
AQuaMoHo
that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core,
AQuaMoHo
exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that
AQuaMoHo
can significantly help annotate the air quality data on a personal scale.
Trireme: Exploration of Hierarchical Multi-level Parallelism for Hardware Acceleration
Authors: Zacharopoulos Georgios , Ejjeh Adel , Jing Ying , Yang En-Yu , Jia Tianyu , Brumar Iulian , Intan Jeremy , Huzaifa Muhammad , Adve Sarita , Adve Vikram , Wei Gu-Yeon , Brooks David
Published: January 2023

The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers must decide which parts of an application to accelerate in hardware and which to leave in software. Moreover, applications in domains such as Extended Reality (XR) offer opportunities for various forms of parallel execution, including loop level, task level, and pipeline parallelism. To assist the design process and expose every possible level of parallelism, we present
Trireme
, a fully automated tool-chain that explores multiple levels of parallelism and produces domain-specific accelerator designs and configurations that maximize performance, given an area budget. FPGA SoCs were used as target platforms, and Catapult HLS [
7
] was used to synthesize RTL using a commercial 12 nm FinFET technology. Experiments on demanding benchmarks from the XR domain revealed a speedup of up to 20×, as well as a speedup of up to 37× for smaller applications, compared to software-only implementations.
New Tools for Lineage Tracing in Cancer In Vivo
Authors: Jones Matthew G. , Yang Dian , Weissman Jonathan S.
Published: January 2023

During tumor evolution, cancer cells can acquire the ability to proliferate, invade neighboring tissues, evade the immune system, and spread systemically. Tracking this process remains challenging, as many key events occur stochastically and over long times, which could be addressed by studying the phylogenetic relationships among cancer cells. Several lineage tracing approaches have been developed and employed in many tumor models and contexts, providing critical insights into tumor evolution. Recent advances in single-cell lineage tracing have greatly expanded the resolution, scale, and readout of lineage tracing toolkits. In this review, we provide an overview of static lineage tracing methods, and then focus on evolving lineage tracing technologies that enable reconstruction of tumor phylogenies at unprecedented resolution. We also discuss in vivo applications of these technologies to profile subclonal dynamics, quantify tumor plasticity, and track metastasis. Finally, we highlight outstanding questions and emerging technologies for building comprehensive cancer evolution roadmaps.
The Sociolinguistic Situation in North Africa: Recognizing and Institutionalizing Tamazight and New Challenges
Authors: Alalou Ali
Published: January 2023

This article reviews several issues that are important for understanding the sociolinguistic situation in North Africa, with an emphasis on Morocco. The article surveys the manner in which North Africa's sociolinguistic profile has evolved over the last two decades (2001–2021). The topics discussed here include the tumultuous and chaotic promotion of monolingualism and the relentless efforts to erase and expunge Amazigh identity from North Africa despite the region's long history of linguistic diversity. Based on an imported ideological slant, these attempts to erase Amazigh identity lasted for decades and contributed to the marginalization of Amazigh people and other minorized communities in the region.
Experimental and Numerical Investigation of the Low-Frequency Inductive Features in Differential PEMFCs: Ionomer Humidification and Platinum Oxide Effects
Authors: Gerling Christophe , Hanauer Matthias , Berner Ulrich , Friedrich K. Andreas
Published: January 2023

The low-frequency inductive features in PEMFC are studied by differential measurements and numerical simulation. Systematic parameter variations are conducted and the discrepancies between the local polarization curve slopes and the capacitive loops of electrochemical impedance spectra (EIS) are evaluated to compute the inductive contributions. These contributions, primarily slow platinum oxide kinetics and ionomer humidification, are disentangled and we show that the latter is more relevant at medium to high currents, leaving mainly kinetics contributions of around 35 mV dec−1 at small currents. We demonstrate that the inductivity reaches over 150 mV dec−1 at high load and that it strongly depends on the current density (j) and on the relative gas humidity (RH), whereas temperature (T) and oxygen partial pressure (

p

O

2

) play a minor role. A new approach for modeling the combination of the oxygen reduction reaction and platinum oxidation leading to inductive loops is presented and integrated into a 1D through-plane model which we parameterize based on our large dataset. We present a comprehensive parameter study with this model. Its current version contains platinum oxide kinetics and electron, proton as well as oxygen transport and yields a good match with both steady-state and EIS data.
Determining the Origin of Lithium Inventory Loss in NMC622||Graphite Lithium Ion Cells Using an LiPF6-Based Electrolyte
Authors: Heidrich Bastian , Stamm Maik , Fromm Olga , Kauling Johanna , Börner Markus , Winter Martin , Niehoff Philip
Published: January 2023

X-ray photoelectron spectroscopy (XPS) is often used in interphase investigations of lithium ion batteries (LIBs). Yet, it is unclear, if its results convey an accurate picture of the lithium loss in aged LIBs. Herein, electrochemical and surface analytical approaches were used to separately quantify the contribution of interphase growth to lithium loss in LIBs. For this, LIB pouch cells (NMC622||graphite, 5 Ah) were aged for 400 full cycles at 20 °C or 60 °C. Electrodes were harvested post mortem and subsequent investigations in lithium metal battery cells showed notably higher reversible and irreversible lithium loss after 60 °C than after 20 °C cycling. While the interphases did not notably increase in thickness with aging, the surface area of both electrodes increased, leading to more electrolyte decomposition and larger lithium loss. Along with the surface area increase, more heterogeneous electrolyte decomposition product residues on the negative electrode surface and higher cathode|electrolyte charge-transfer resistances were observed. In conclusion, the applied combination of XPS and nitrogen adsorption can quantify homogenously distributed electrolyte decomposition layers of thicknesses <10 nm, but not thick and heterogeneous decomposition product residues arising with 60 °C cycling. For this, the need for an alternative quantification method is highlighted.
Review—Study on Catalyst in Zn-Air Batteries: Bibliometric Method
Authors: Zhang Cheng , Shen Hao , Li Site , Li Wenhao , Liu Xiaoya , Fu Zaiguo , Wu Jiang , Ma Xinxia , Wu Maoliang , Qi Yongfeng
Published: January 2023

Bibliometric methods are used to summarize literature on cathode electrocatalysts for Zn-air batteries published from 2007 to 2021 and analyze the characteristics and research trends of the published literature. From 2007 to 2013, the number of articles published every year has been tiny. From then to now, the number of papers published increased rapidly. According to statistics in the past six years, China has published the most significant number of articles, accounting for almost two-thirds of the total.
Novel (E)−2-((1-(thiophen-2-yl)ethylidene)-amino) phenol Manganese(II) as an Ionophore Based on Thiocyanate-Selective Electrodes and Its Applications
Authors: Ayoub Manara A. , Abd-Elnasser Eman H. , Ahmed Mona A. , Rizk Mariam G.
Published: January 2023

Novel highly selective potentiometric sensor based on aquadichloro(E)−2-((1-(thiophen-2-yl)ethylidene)-amino)phenol manganese(II)trihydrate as an anion carrier. The thiocayanate electrode displayed a very high selectivity compared with others inorganic anions. Different sensors with plasticized PVC membranes have been investigated. The sensors construction containing different amounts of ionophore with and without additives. The pH over 3.5–6.5 range has been studied. Optimized membrane electrode included 66 mg PVC, 132 mg o-nitrophenyloctylether, 10 mol % tetrakis(trifluoromethyl)phenyl borate and 2% [Mn(C12H11NOS)(Cl2)(H2O)]· 3H2O. The optimized sensors exhibit Nernstian response for thiocyanate through a linear concentration ranging from (5 × 10−8 to 9.06 × 10−1 M) with a detection limit of 3 × 10−8 M and a slope of −57.7 mV decade−1, the measurement carried out in acetate buffer pH 4.7. The response time of electrode <10 s and the lifetime of the sensor more than 6 weeks. The proposed electrode was effectively utilized to estimation of thiocyanate in saliva sample, the results revealed a valid agreement with reference colorimetric method.
α1 Adrenoreceptor antagonism mitigates extracellular mitochondrial DNA accumulation in lung fibrosis models and in patients with idiopathic pulmonary fibrosis
Authors: Ishikawa Genta , Peng Xueyan , McGovern John , Woo Sam , Perry Carrighan , Liu Angela , Yu Sheeline , Ghincea Alexander , Kishchanka Aliaksandr , Fiorini Vitória , Hu Buqu , Sun Ying , Sun Huanxing , Ryu Changwan , Herzog Erica L.
Published: January 2023

Idiopathic pulmonary fibrosis is increasingly associated with nerve-driven processes and endogenous innate immune ligands such as mitochondrial DNA (mtDNA). Interestingly, a connection between these entities has not been explored. Here, we report that noradrenaline (NA) derived from the lung’s adrenergic nerve supply drives α-smooth muscle actin (αSMA)-expressing fibroblast accumulation via mechanisms involving α1 adrenoreceptors and mtDNA. Using the bleomycin model, we compared ablation of the lung’s adrenergic nerve supply with surgical adrenal resection and found that NA derived from local but not adrenal sources contributes to experimentally induced lung fibrosis and the emergence of an αSMA+ve fibroblast population expressing adrenoreceptor α-1D (ADRA1D). Therapeutic delivery of the α1 adrenoreceptor antagonist terazosin reversed these changes and suppressed extracellular mtDNA accumulation. Cultured normal human lung fibroblasts displayed α1 adrenoreceptors and in response to costimulation with TGFβ1 and NA adopted ACTA2 expression and extracellular mtDNA release. These findings were opposed by terazosin. Evaluation of a previously studied IPF cohort revealed that patients prescribed α1 adrenoreceptor antagonists for nonpulmonary indications demonstrated improved survival and reduced concentrations of plasma mtDNA. Our observations link nerve-derived NA, α1 adrenoreceptors, extracellular mtDNA, and lung fibrogenesis in mouse models, cultured cells, and humans with IPF. Further study of this neuroinnate connection may yield new avenues for investigation in the clinical and basic science realms.
Single‐Machine Group Scheduling Problems with General Deterioration and Linear Learning Effects
Authors: Ning Lei , Sun Li
Published: January 2023

In this paper, we introduce a group scheduling model with deterioration and a general linear learning effect. The group setup time is a linear deterioration function of its starting time. The general learning effect is time‐dependent and position‐dependent. The objectives are to minimize the makespan, total completion time, and total weighted completion time, respectively. We show the problems remain solvable in polynomial time under the proposed model.
Experimental Investigation on Damage Characteristics and Early Warning of Frozen Coal under Uniaxial Compression
Authors: Sun Xiang , Liu Bin , Dong Qi , Shi Feng
Published: January 2023

This article conducted acoustic emission tests of natural coal, natural frozen coal, and saturated frozen coal to explore the damage characteristics of frozen coal and in view of the frozen coal damage early warning problems and explored the strength deformation and acoustic emission characteristics of coal samples. The test results showed that the compressive strength of the frozen coal samples decreased significantly, and the strength of the saturated frozen coal samples was the lowest; more acoustic emissions were generated at the initial loading stage. The damage parameters were defined using the acoustic emission parameters. The damage characteristics of the coal samples were analyzed. The damage model of the coal samples was established based on the power function. The early warning parameter VAEi to analyze the evolution characteristics of the variance curve was proposed; the sudden jump point b of VAEi from the stable development stage to the rapid growth stage was defined as a general early warning point; the endpoint point c of the parameter rapid growth stage was defined as a key early warning point; a zonal and hierarchical early warning was finally proposed.
Effects of the MR-DTI Characteristics of the Trigeminal Ganglion Target on Radiofrequency Treatment in Patients with Trigeminal Neuralgia: A Retrospective Observational Clinical Study
Authors: Su Xu , Wang Zhengming , Cheng Min , Tian Yu , Du Chao
Published: January 2023

Background. In the percutaneous treatment of trigeminal neuralgia (TN), the difficulty in accessing the foramen ovale (FO) has been widely recognized. However, the most efficient percutaneous treatment target is the so-called trigeminal ganglion target (TGT). We propose that the TGT in a puncture can be identified by magnetic resonance diffusion tensor imaging (MR-DTI). Objectives. To observe the effect of the characteristics of the TGT as detected by MR-DTI on percutaneous stereotactic radiofrequency rhizotomy (PSR) in TN patients. Methods. In our observational study, we preoperatively performed MR-DTI and/or 3D-CT for 48 TN patients, analyzed the characteristics of the TGT and/or FO, and designed appropriate surgical schemes for producing an accurate PSR trajectory according to these characteristics. The position and size of the TGT aided in adjusting the puncture angle and guiding the approach. Then, we successfully performed a customized PSR guided by the characteristics of the FO or TGT. During the postoperative and follow-up periods, we assessed the effect of treatment with pain scores and MR-DTI findings. Results. The characteristics of the TGT vary from patient to patient. We performed PSR with a single puncture guided by MR-DTI and 3D-CT in 16 patients, and only one patient required three punctures. All three of these punctures reached the FO target, as confirmed by intraoperative C-arm X-ray. We finally reached the TGT successfully after 2 additional attempts, confirming that the probe reached the TGT that accurately covered the pain territory with an electrophysiology test. The characteristics of the TGT were negatively correlated with the number of PSR punctures. Fewer complications occurred for PSRs guided by the TGT than for PSRs guided by the FO. Conclusions. The characteristics of the TGT are correlated with the number of punctures in the PSR. The application of MR-DTI for detecting the size of the TGT is an important step in predicting the difficulty of puncture. The PSR approach can be guided by the TGT and FO for TN patients who present with multiple adverse factors and thus may be beneficial in reducing the number of complications.
The Characterisation of Fracture Resistance of Asphalt Mixtures Containing Rubber Modifiers and a Wax-Based Additive
Authors: Poovaneshvaran Sharvin , Mohd Hasan Mohd Rosli , Jamshidi Ali , Mohd Ghazali Mohd Fahmi Haikal , Yang Xu , Putra Jaya Ramadhansyah
Published: January 2023

Asphalt mixture modification with rubberised material frequently results in improved characteristics and extended service life in actual application. This research characterised the synergistic consequences of rubber modifiers (crumb rubber powder (CRP) and natural rubber latex (NRL)) and wax-based admixtures (Tough Fix Hyper (TFH)) on the performance of the asphalt mixture from the fracture energy and laboratory fracture resistance perspectives. Semicircular bending (SCB) and indirect tensile strength (ITS) tests were conducted to assess the fracture properties of the asphalt mixture samples. To prepare asphalt mixture samples, the wet method was utilised. Higher CRP levels resulted in greater strength and a longer time to attain peak force for both control and mixtures containing wax admixture, as determined by SCB. The interaction between the higher CRP or NRL content and the TFH additive enhanced the fracture resistance, indicating that the components are highly compatible. The 10L + TFH additive produced the highest fraction of energy, indicating a more significant improvement than the counterpart mixes containing the CRP modifier. In addition, incorporation of the CRP and NRL increased the fracture plastic zone (FPZ), resulting in increased fracture toughness. Therefore, the gradient of fracture toughness and fracture energy in the asphalt mix depends on the rubber type, content, and TFH. Although the higher CRP, NRL, and TFH improve the fracture energy and cracking resistance, they increase the crack initiation and propagation velocities, whereby the high bitumen stiffness makes the mixture more brittle than the control mixture. Caution should be exercised when selecting the content of rubber modifier and TFH for the asphalt pavements in low-temperature service. Also, there is a direct interconnection between fracture resistance and fracture energy in the mixtures containing CRP, NRL, and TFH. Such correlations can be used as the premise of predictive micro- and macro-models to evaluate mixture performance in terms of fracture resistance.
Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time
Authors: Kang Lu , Liu Ding , Wu Yali , Ping Guozheng
Published: January 2023

Considering the widely existing processing time uncertainty in the real‐world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two‐stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.
Apoptotic Responses Mediated by Nostoc-Synthesized Silver Nanoparticles against Ehrlich Ascites Carcinoma Tumor-Bearing Mice
Authors: Hamida Reham Samir , Albasher Gadah , Bin-Meferij Mashael Mohammed
Published: January 2023

Among the metallic nanoparticles (NPs), silver NPs are the leading anticancer nanoagents due to their physicochemical properties and inhibitory activities against different cancers. Many reports exhibited the cytotoxic effect of silver-NPs fabricated by cyanobacteria against various cancer cell lines. However, their lethal mechanisms have not been elucidated. The antiproliferative and cytotoxic effects of silver-NPs synthesized previously using Nostoc Bahar M (N-SNPs), and their associated mechanisms were evaluated in the solid Ehrlich ascites carcinoma (EAC) tumor-bearing mice model. Blood parameters, liver enzymes (alanine transaminase, aspartate aminotransferase, and alkaline phosphatase), lactate dehydrogenase, adenosine triphosphate, and oxidative stress markers, including glutathione, glutathione peroxidase, catalase, and malondialdehyde, were analyzed. In addition, the expression of pro- and antiapoptotic genes and proteins was screened utilizing quantitative real-time polymerase chain reaction and western blotting. N-SNPs significantly reduced tumor size in mice without affecting body weight and did not compromise liver function or alter blood parameters. Besides, N-SNPs negatively impact membrane integrity and metabolic activity and significantly enhance oxidative stress by causing an imbalance in antioxidant activity in tumor tissues. Intriguingly, N-SNPs-stimulated apoptosis signaling pathways via a combination of enhanced and suppressed responses of pro- and antiapoptotic genes and proteins. The cytotoxic activity and apoptosis effect of N-SNPs in tumor tissue could be attributed to the surge in reactive oxygen species production and depletion of antioxidant activity. Certainly, N-SNPs could provide robust antitumor agents against solid tumors.