bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bridge RNAs direct programmable recombination of target and donor DNA
Authors: Durrant Matthew G. , Perry Nicholas T. , Pai James J. , Jangid Aditya R. , Athukoralage Januka S. , Hiraizumi Masahiro , McSpedon John P. , Pawluk April , Nishimasu Hiroshi , Konermann Silvana , Hsu Patrick D. 
Published: June 2024

AbstractGenomic rearrangements, encompassing mutational changes in the genome such as insertions, deletions or inversions, are essential for genetic diversity. These rearrangements are typically orchestrated by enzymes that are involved in fundamental DNA repair processes, such as homologous recombination, or in the transposition of foreign genetic material by viruses and mobile genetic elements1,2. Here we report that IS110 insertion sequences, a family of minimal and autonomous mobile genetic elements, express a structured non-coding RNA that binds specifically to their encoded recombinase. This bridge RNA contains two internal loops encoding nucleotide stretches that base-pair with the target DNA and the donor DNA, which is the IS110 element itself. We demonstrate that the target-binding and donor-binding loops can be independently reprogrammed to direct sequence-specific recombination between two DNA molecules. This modularity enables the insertion of DNA into genomic target sites, as well as programmable DNA excision and inversion. The IS110 bridge recombination system expands the diversity of nucleic-acid-guided systems beyond CRISPR and RNA interference, offering a unified mechanism for the three fundamental DNA rearrangements—insertion, excision and inversion—that are required for genome design.
Chemical reservoir computation in a self-organizing reaction network
Authors: Baltussen Mathieu G. , de Jong Thijs J. , Duez Quentin , Robinson William E. , Huck Wilhelm T. S. 
Published: June 2024

AbstractChemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.
VHEE FLASH sparing effect measured at CLEAR, CERN with DNA damage of pBR322 plasmid as a biological endpoint
Authors: Wanstall Hannah C. , Korysko Pierre , Farabolini Wilfred , Corsini Roberto , Bateman Joseph J. , Rieker Vilde , Hemming Abigail , Henthorn Nicholas T. , Merchant Michael J. , Santina Elham , Chadwick Amy L. , Robertson Cameron , Malyzhenkov Alexander , Jones Roger M. 
Published: June 2024

AbstractUltra-high dose rate (UHDR) irradiation has been shown to have a sparing effect on healthy tissue, an effect known as ‘FLASH’. This effect has been studied across several radiation modalities, including photons, protons and clinical energy electrons, however, very little data is available for the effect of FLASH with Very High Energy Electrons (VHEE). pBR322 plasmid DNA was used as a biological model to measure DNA damage in response to Very High Energy Electron (VHEE) irradiation at conventional (0.08 Gy/s), intermediate (96 Gy/s) and ultra-high dose rates (UHDR, (2 × 109 Gy/s) at the CERN Linear Electron Accelerator (CLEAR) user facility. UHDRs were used to determine if the biological FLASH effect could be measured in the plasmid model, within a hydroxyl scavenging environment. Two different concentrations of the hydroxyl radical scavenger Tris were used in the plasmid environment to alter the proportions of indirect damage, and to replicate a cellular scavenging capacity. Indirect damage refers to the interaction of ionising radiation with molecules and species to generate reactive species which can then attack DNA. UHDR irradiated plasmid was shown to have significantly reduced amounts of damage in comparison to conventionally irradiated, where single strand breaks (SSBs) was used as the biological endpoint. This was the case for both hydroxyl scavenging capacities. A reduced electron energy within the VHEE range was also determined to increase the DNA damage to pBR322 plasmid. Results indicate that the pBR322 plasmid model can be successfully used to explore and test the effect of UHDR regimes on DNA damage. This is the first study to report FLASH sparing with VHEE, with induced damage to pBR322 plasmid DNA as the biological endpoint. UHDR irradiated plasmid had reduced amounts of DNA single-strand breaks (SSBs) in comparison with conventional dose rates. The magnitude of the FLASH sparing was a 27% reduction in SSB frequency in a 10 mM Tris environment and a 16% reduction in a 100 mM Tris environment.
Lead-free Cs2Ag1−xNaxIn1 − yBiyCl6 perovskite films with broad warm-yellow emission for lighting applications
Authors: Wang Haiyan , Chen Jin , Sun Yu , Wang Fengchao , Yang Jing , Zhang Canyun , Kong Jinfang , Li Lan 
Published: June 2024

AbstractLead-free halide double perovskite Cs2AgInCl6 has been extensively studied in recent years due to the lead toxicity and poor stability of common lead halide perovskites. In this study, sodium (Na+) and bismuth (Bi3+) doped into Cs2AgInCl6 double perovskite, then Cs2Ag1−xNaxIn1 − yBiyCl6 films with broadband warm-yellow emissions were achieved by the blade coating method. Herein, Na and Bi content were changed as variables at a series of parameter optimization experiments, respectively. In the Cs2Ag1−xNaxIn1 − yBiyCl6 systems, Na+ broke the parity-forbidden transition of Cs2AgInCl6, and Bi3+ suppressed non-radiative recombination. The partial replacement of Ag+ with Na+ ions and doping with Bi3+ cations were crucial for increasing the intensity of the PL emission. The experimental results showed that the photoluminescence quantum yield of the Cs2Ag0.4Na0.6In0.8Bi0.2Cl6 film was 66.38%, which was the highest data among all samples. It demonstrated remarkable stability under heat and ultraviolet conditions. After five thermal cycles, the PL intensity of the Cs2Ag0.4Na0.6In0.8Bi0.2Cl6 film is only reduced to approximately 5.7% of the initial value. After 720 h continuous ultraviolet irradiation, there occurred 31.9% emission decay of the film.
Introducing the antibacterial and photocatalytic degradation potentials of biosynthesized chitosan, chitosan–ZnO, and chitosan–ZnO/PVP nanoparticles
Authors: Aouadi Abdelatif , Hamada Saud Djamila , Rebiai Abdelkrim , Achouri Abdelhak , Benabdesselam Soulef , Mohamed Abd El-Mordy Fatma , Pohl Pawel , Ahmad Sheikh F. , Attia Sabry M. , Abulkhair Hamada S. , Ararem Abderrahmane , Messaoudi Mohammed 
Published: June 2024

AbstractThe development of nanomaterials has been speedily established in recent years, yet nanoparticles synthesized by traditional methods suffer unacceptable toxicity and the sustainability of the procedure for synthesizing such nanoparticles is inadequate. Consequently, green biosynthesis, which employs biopolymers, is gaining attraction as an environmentally sound alternative to less sustainable approaches. Chitosan-encapsulated nanoparticles exhibit exceptional antibacterial properties, offering a wide range of uses. Chitosan, obtained from shrimp shells, aided in the environmentally friendly synthesis of high-purity zinc oxide nanoparticles (ZnO NPs) with desirable features such as the extraction yield (41%), the deacetylation (88%), and the crystallinity index (74.54%). The particle size of ZnO NPs was 12 nm, while that of chitosan–ZnO NPs was 21 nm, and the bandgap energies of these nanomaterials were 3.98 and 3.48, respectively. The strong antibacterial action was demonstrated by ZnO NPs, chitosan–ZnO NPs, and chitosan–ZnO/PVP, particularly against Gram-positive bacteria, making them appropriate for therapeutic use. The photocatalytic degradation abilities were also assessed for all nanoparticles. At a concentration of 6 × 10–5 M, chitosan removed 90.5% of the methylene blue (MB) dye, ZnO NPs removed 97.4%, chitosan-coated ZnO NPs removed 99.6%, while chitosan–ZnO/PVP removed 100%. In the case of toluidine blue (TB), at a concentration of 4 × 10–3 M, the respective efficiencies were 96.8%, 96.8%, 99.5%, and 100%, respectively. Evaluation of radical scavenger activity revealed increased scavenging of ABTS and DPPH radicals by chitosan–ZnO/PVP compared to individual zinc oxide or chitosan–ZnO, where the IC50 results were 0.059, 0.092, 0.079 mg/mL, respectively, in the ABTS test, and 0.095, 0.083, 0.061, and 0.064 mg/mL in the DPPH test, respectively. Moreover, in silico toxicity studies were conducted to predict the organ-specific toxicity through ProTox II software. The obtained results suggest the probable safety and the absence of organ-specific toxicity with all the tested samples.
Investigation of deformation behavior and strain-induced precipitations in Al–Zn–Mg–Cu alloys across a wide temperature range
Authors: Zhang Qingdong , Zuo Jinrong , Yang Chen , Xia Yingxiang , Shu Xuedao , Mei Bizhou , Wang Ying , Cui Long 
Published: June 2024

AbstractThis study explores the hot deformation behavior of Al–Zn–Mg–Cu alloy through uniaxial hot compression (200 °C–450°C) using the Gleeble-1500. True stress–strain curves were corrected, and three models were established: the Arrhenius model, strain compensated (SC) Arrhenius model, and strain compensated recrystallization temperature (RT) segmentation-based (TS-SC) Arrhenius model. Comparative analysis revealed the limited predictive accuracy of the SC Arrhenius model, with a 25.12% average absolute relative error (AARE), while the TS-SC Arrhenius model exhibited a significantly improved to 9.901% AARE. Material parameter calculations displayed variations across the temperature range. The SC Arrhenius model, utilizing an average slope method for parameter computation, failed to consider temperature-induced disparities, limiting its predictive capability. Hot processing map, utilizing the Murty improved Dynamic Materials Model (DMM), indicated optimal conditions for stable forming of the Al–Zn–Mg–Cu alloy. Microstructural analysis revealed MgZn2 precipitation induced by hot deformation, with crystallographic defects enhancing nucleation rates and precipitate refinement.
FADS1/2 control lipid metabolism and ferroptosis susceptibility in triple-negative breast cancer
Authors: Lorito Nicla , Subbiani Angela , Smiriglia Alfredo , Bacci Marina , Bonechi Francesca , Tronci Laura , Romano Elisabetta , Corrado Alessia , Longo Dario Livio , Iozzo Marta , Ippolito Luigi , Comito Giuseppina , Giannoni Elisa , Meattini Icro , Avgustinova Alexandra , Chiarugi Paola , Bachi Angela , Morandi Andrea 
Published: June 2024

AbstractTriple-negative breast cancer (TNBC) has limited therapeutic options, is highly metastatic and characterized by early recurrence. Lipid metabolism is generally deregulated in TNBC and might reveal vulnerabilities to be targeted or used as biomarkers with clinical value. Ferroptosis is a type of cell death caused by iron-dependent lipid peroxidation which is facilitated by the presence of polyunsaturated fatty acids (PUFA). Here we identify fatty acid desaturases 1 and 2 (FADS1/2), which are responsible for PUFA biosynthesis, to be highly expressed in a subset of TNBC with a poorer prognosis. Lipidomic analysis, coupled with functional metabolic assays, showed that FADS1/2 high-expressing TNBC are susceptible to ferroptosis-inducing agents and that targeting FADS1/2 by both genetic interference and pharmacological approach renders those tumors ferroptosis-resistant while unbalancing PUFA/MUFA ratio by the supplementation of exogenous PUFA sensitizes resistant tumors to ferroptosis induction. Last, inhibiting lipid droplet (LD) formation and turnover suppresses the buffering capacity of LD and potentiates iron-dependent cell death. These findings have been validated in vitro and in vivo in mouse- and human-derived clinically relevant models and in a retrospective cohort of TNBC patients.
High-throughput light-induced immunoassay with milliwatt-level laser under one-minute optical antibody-coating on nanoparticle-imprinted substrate
Authors: Kanoda Masatoshi , Hayashi Kota , Takagi Yumiko , Tamura Mamoru , Tokonami Shiho , Iida Takuya 
Published: June 2024

AbstractThe efficient detection of protein biomarkers is critical for public health. However, the sensitivity of conventional antigen test kits is relatively low for early diagnosis, and laboratory immunoassays require complex pretreatment processes overnight. If target nanomaterials could be remotely guided to the detection site, simpler and faster methods would be developed. Here, we reveal the mechanism of light-induced immunoassay that anti-spike-protein antibodies for SARS-CoV-2 were coated on our developed nanoparticle-imprinted plasmonic substrate (NPI-PS) over the submillimeter area within one minute and nanoparticles modified with spike proteins can be selectively detected within a few minutes at one or two orders of higher sensitivity via a two-step optical condensation using NPI-PS. NPI-PS exhibits high-performance optical condensation with high photothermal properties even under milliwatt-class nonresonant laser irradiation, enabling a wide range of quantitative measurements. These findings support an innovative strategy to mitigate pandemic threats and various diseases through the high-throughput detection of protein biomarkers.
Vascular remodelling in cardiovascular diseases: hypertension, oxidation, and inflammation
Authors: Totoń-Żurańska Justyna , Mikolajczyk Tomasz P. , Saju Blessy , Guzik Tomasz J. 
Published: June 2024

Abstract
Optimal vascular structure and function are essential for maintaining the physiological functions of the cardiovascular system. Vascular remodelling involves changes in vessel structure, including its size, shape, cellular and molecular composition. These changes result from multiple risk factors and may be compensatory adaptations to sustain blood vessel function. They occur in diverse cardiovascular pathologies, from hypertension to heart failure and atherosclerosis. Dynamic changes in the endothelium, fibroblasts, smooth muscle cells, pericytes or other vascular wall cells underlie remodelling. In addition, immune cells, including macrophages and lymphocytes, may infiltrate vessels and initiate inflammatory signalling. They contribute to a dynamic interplay between cell proliferation, apoptosis, migration, inflammation, and extracellular matrix reorganisation, all critical mechanisms of vascular remodelling. Molecular pathways underlying these processes include growth factors (e.g., vascular endothelial growth factor and platelet-derived growth factor), inflammatory cytokines (e.g., interleukin-1β and tumour necrosis factor-α), reactive oxygen species, and signalling pathways, such as Rho/ROCK, MAPK, and TGF-β/Smad, related to nitric oxide and superoxide biology. MicroRNAs and long noncoding RNAs are crucial epigenetic regulators of gene expression in vascular remodelling. We evaluate these pathways for potential therapeutic targeting from a clinical translational perspective. In summary, vascular remodelling, a coordinated modification of vascular structure and function, is crucial in cardiovascular disease pathology.
Lexical Morphology as a Source of Risk and Resilience for Learning to Read With Dyslexia: An fNIRS Investigation
Authors: Eggleston Rachel L. , Marks Rebecca A. , Sun Xin , Yu Chi-Lin , Zhang Kehui , Nickerson Nia , Hu Xiaosu , Caruso Valeria , Kovelman Ioulia 
Published: June 2024

Purpose:
We examined the neurocognitive bases of lexical morphology in children of varied reading abilities to understand the role of meaning-based skills in learning to read with dyslexia.

Method:

Children completed auditory morphological and phonological awareness tasks during functional near-infrared spectroscopy neuroimaging. We first examined the relation between lexical morphology and phonological processes in typically developing readers (Study 1,
N
= 66,
M
age
= 8.39), followed by a more focal inquiry into lexical morphology processes in dyslexia (Study 2,
N
= 50,
M
age
= 8.62).

Results:

Typical readers exhibited stronger engagement of language neurocircuitry during the morphology task relative to the phonology task, suggesting that morphological analyses involve synthesizing multiple components of sublexical processing. This effect was stronger for more analytically complex derivational affixes (
like + ly
) than more semantically transparent free base morphemes (
snow + man
). In contrast, children with dyslexia exhibited stronger activation during the free base condition relative to derivational affix condition. Taken together, the findings suggest that although children with dyslexia may struggle with derivational morphology, they may also use free base morphemes' semantic information to boost word recognition.

Conclusion:
This study informs literacy theories by identifying an interaction between reading ability, word structure, and how the developing brain learns to recognize words in speech and print.

Supplemental Material:

https://doi.org/10.23641/asha.25944949
DualAD: Dual adversarial network for image anomaly detection⋆
Authors: Wan Yonghao , Feng Aimin 
Published: June 2024

AbstractAnomaly Detection, also known as outlier detection, is critical in domains such as network security, intrusion detection, and fraud detection. One popular approach to anomaly detection is using autoencoders, which are trained to reconstruct input by minimising reconstruction error with the neural network. However, these methods usually suffer from the trade‐off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. The authors find that the above trade‐off can be better mitigated by imposing constraints on the latent space of images. To this end, the authors propose a new Dual Adversarial Network (DualAD) that consists of a Feature Constraint (FC) module and a reconstruction module. The method incorporates the FC module during the reconstruction training process to impose constraints on the latent space of images, thereby yielding feature representations more conducive to anomaly detection. Additionally, the authors employ dual adversarial learning to model the distribution of normal data. On the one hand, adversarial learning was implemented during the reconstruction process to obtain higher‐quality reconstruction samples, thereby preventing the effects of blurred image reconstructions on model performance. On the other hand, the authors utilise adversarial training of the FC module and the reconstruction module to achieve superior feature representation, making anomalies more distinguishable at the feature level. During the inference phase, the authors perform anomaly detection simultaneously in the pixel and latent spaces to identify abnormal patterns more comprehensively. Experiments on three data sets CIFAR10, MNIST, and FashionMNIST demonstrate the validity of the authors’ work. Results show that constraints on the latent space and adversarial learning can improve detection performance.
A deep model‐based channel interference mitigation for OTFS signals in ISAC systems
Authors: Zhou Wenkai , Wang Wenming , Qi Wanbin , Zhang Ronghui 
Published: June 2024

AbstractIn recent years, Orthogonal Time Frequency Space Modulation (OTFS) has gained popularity in integrated sensing and communications (ISAC) system due to its robustness against Doppler offset and delay changes. Traditional pilot‐based methods for accurate channel parameter estimation are complex and struggle with rapidly changing channel conditions. In this letter, a deep encode‐decode network (DED‐Net) is proposed. It uses DL to automatically learn and eliminate channel interference from OTFS signals. The framework employs a deep encoding and decoding network, similar to a filter, learning complex signal features to effectively remove interference. Our experiments demonstrate DED‐Net's ability to eliminate interference in OTFS modulation signals, offering an alternative to pilot‐based methods and showcasing DL's potential for ISAC systems.
Feature ensemble network for medical image segmentation with multi‐scale atrous transformer
Authors: Gai Di , Geng Yuhan , Huang Xia , Huang Zheng , Xiong Xin , Zhou Ruihua , Wang Qi 
Published: June 2024

AbstractRecent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation.
Absolute velocity estimation of UAVs based on phase correlation and monocular vision in unknown GNSS‐denied environments
Authors: Deng Heng , Li Duhao , Shen Boyang , Zhao Zhiyao , Arif Usman 
Published: June 2024

AbstractThis paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.