bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ribosome biogenesis in disease: new players and therapeutic targets
Authors: Jiao Lijuan , Liu Yuzhe , Yu Xi-Yong , Pan Xiangbin , Zhang Yu , Tu Junchu , Song Yao-Hua , Li Yangxin 
Published: January 2023

AbstractThe ribosome is a multi-unit complex that translates mRNA into protein. Ribosome biogenesis is the process that generates ribosomes and plays an essential role in cell proliferation, differentiation, apoptosis, development, and transformation. The mTORC1, Myc, and noncoding RNA signaling pathways are the primary mediators that work jointly with RNA polymerases and ribosome proteins to control ribosome biogenesis and protein synthesis. Activation of mTORC1 is required for normal fetal growth and development and tissue regeneration after birth. Myc is implicated in cancer development by enhancing RNA Pol II activity, leading to uncontrolled cancer cell growth. The deregulation of noncoding RNAs such as microRNAs, long noncoding RNAs, and circular RNAs is involved in developing blood, neurodegenerative diseases, and atherosclerosis. We review the similarities and differences between eukaryotic and bacterial ribosomes and the molecular mechanism of ribosome-targeting antibiotics and bacterial resistance. We also review the most recent findings of ribosome dysfunction in COVID-19 and other conditions and discuss the consequences of ribosome frameshifting, ribosome-stalling, and ribosome-collision. We summarize the role of ribosome biogenesis in the development of various diseases. Furthermore, we review the current clinical trials, prospective vaccines for COVID-19, and therapies targeting ribosome biogenesis in cancer, cardiovascular disease, aging, and neurodegenerative disease.
Inhibition of acetyl-CoA carboxylase impaired tubulin palmitoylation and induced spindle abnormalities
Authors: Fang Chieh-Ting , Kuo Hsiao-Hui , Amartuvshin Oyundari , Hsu Hwei-Jan , Liu Sih-Long , Yao Jhong-Syuan , Yih Ling-Huei 
Published: January 2023

AbstractTubulin s-palmitoylation involves the thioesterification of a cysteine residue in tubulin with palmitate. The palmitate moiety is produced by the fatty acid synthesis pathway, which is rate-limited by acetyl-CoA carboxylase (ACC). While it is known that ACC is phosphorylated at serine 79 (pSer79) by AMPK and accumulates at the spindle pole (SP) during mitosis, a functional role for tubulin palmitoylation during mitosis has not been identified. In this study, we found that modulating pSer79-ACC level at the SP using AMPK agonist and inhibitor induced spindle defects. Loss of ACC function induced spindle abnormalities in cell lines and in germ cells of theDrosophilagermarium, and palmitic acid (PA) rescued the spindle defects in the cell line treated transiently with the ACC inhibitor, TOFA. Furthermore, inhibition of protein palmitoylating or depalmitoylating enzymes also induced spindle defects. Together, these data suggested that precisely regulated cellular palmitate level and protein palmitoylation may be required for accurate spindle assembly. We then showed that tubulin was largely palmitoylated in interphase cells but less palmitoylated in mitotic cells. TOFA treatment diminished tubulin palmitoylation at doses that disrupt microtubule (MT) instability and cause spindle defects. Moreover, spindle MTs comprised of α-tubulins mutated at the reported palmitoylation site exhibited disrupted dynamic instability. We also found that TOFA enhanced the MT-targeting drug-induced spindle abnormalities and cytotoxicity. Thus, our study reveals that precise regulation of ACC during mitosis impacts tubulin palmitoylation to delicately control MT dynamic instability and spindle assembly, thereby safeguarding nuclear and cell division.
Providing normative information increases intentions to accept a COVID-19 vaccine
Authors: Moehring Alex , Collis Avinash , Garimella Kiran , Rahimian M. Amin , Aral Sinan , Eckles Dean 
Published: January 2023

AbstractDespite the availability of multiple safe vaccines, vaccine hesitancy may present a challenge to successful control of the COVID-19 pandemic. As with many human behaviors, people’s vaccine acceptance may be affected by their beliefs about whether others will accept a vaccine (i.e., descriptive norms). However, information about these descriptive norms may have different effects depending on the actual descriptive norm, people’s baseline beliefs, and the relative importance of conformity, social learning, and free-riding. Here, using a pre-registered, randomized experiment (N = 484,239) embedded in an international survey (23 countries), we show that accurate information about descriptive norms can increase intentions to accept a vaccine for COVID-19. We find mixed evidence that information on descriptive norms impacts mask wearing intentions and no statistically significant evidence that it impacts intentions to physically distance. The effects on vaccination intentions are largely consistent across the 23 included countries, but are concentrated among people who were otherwise uncertain about accepting a vaccine. Providing normative information in vaccine communications partially corrects individuals’ underestimation of how many other people will accept a vaccine. These results suggest that presenting people with information about the widespread and growing acceptance of COVID-19 vaccines helps to increase vaccination intentions.
Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach
Authors: Yang Ning , Yuan Shuyi , Li Chunlong , Xiao Wenqing , Xie Shuangcong , Li Liming , Jiang Guihua , Ma Xiaofen 
Published: January 2023

AbstractThis study investigated whether the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) features could be used as potentially neurological markers to identify chronic insomnia (CI) using resting-state functional MRI and machine learning method logistic regression (LR). This study included 49 CI patients and 47 healthy controls (HC). Voxel-wise features, including the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were extracted from resting-state functional magnetic resonance brain images. Then, we divided the data into two independent cohorts for training (44 CI patients and 42 HC patients), and independent validation (5 CI patients and 5 HC patients) by using logistic regression. The model was evaluated using 20 rounds of fivefold cross‑validation for training. In particular, a two-sample t-test (GRF corrected, p-voxel < 0.001, p-cluster < 0.05) was used for feature selection during the model training. Finally, single‑shot testing of the final model was performed on the independent validation cohort. A correlation analysis (Bonferroni correction, p < 0.05/4) was also conducted to determine whether the features contributing to the prediction were correlated with clinical characteristics, including the Insomnia Severity Index (ISI), Pittsburgh sleep quality index (PSQI), self-rating anxiety scale (SAS), and self-rating depression scale (SDS). Results showed that resting-state features had a discrimination accuracy of 86.40%, with a sensitivity of 93.00% and specificity of 79.80%. The area under the curve (AUC) was 0.89 (all $${P}_{\mathrm{permutation}}$$

P
permutation

< 0.001). The ALFF and FC features showed significant differences between the CI patients and HC. The regions contributing to the prediction mainly included the anterior cingulate, prefrontal cortex, orbital part of the frontal lobe, angular gyrus, cingulate gyrus, praecuneus, parietal lobe, temporal gyrus, superior temporal gyrus, and middle temporal gyrus. Furthermore, some specific functional connectivity among related regions was positively correlated with the ISI, and also negatively related to the SDS in correlation analysis. Our current study suggested that ALFF and FC in the regions contributing to diagnostic identification might serve as potential neuromarkers for CI.
Data-driven decarbonisation pathways for reducing life cycle GHG emissions from food waste in the hospitality and food service sectors
Authors: Cheng I Kit , Leong Kin K. 
Published: January 2023

AbstractThe Hospitality and Food Service (HaFS) sectors are notoriously known for their contribution to the food waste problem. Hence, there is an urgent need to devise strategies to reduce food waste in the HaFS sectors and to decarbonise their operation to help fight hunger, achieve food security, improve nutrition and mitigate climate change. This study proposes three streams to decarbonise the staff cafeteria operation in an integrated resort in Macau. These include upstream optimisation to reduce unserved food waste, midstream education to raise awareness amongst staff about the impact of food choices on the climate and health, and finally downstream recognition to reduce edible plate waste using a state-of-the-art computer vision system. Technology can be an effective medium to facilitate desired behavioural change through nudging, much like how speed cameras can cause people to slow down and help save lives. The holistic and data-driven approach taken revealed great potential for organisations or institutions that offer catering services to reduce their food waste and associated carbon footprint whilst educating individuals about the intricate link between food, climate and well-being.
Shortcut to synchronization in classical and quantum systems
Authors: Impens François , Guéry-Odelin David 
Published: January 2023

AbstractSynchronization is a major concept in nonlinear physics. In a large number of systems, it is observed at long times for a sinusoidal excitation. In this paper, we design a transiently non-sinusoidal driving to reach the synchronization regime more quickly. We exemplify an inverse engineering method to solve this issue on the classical Van der Pol oscillator. This approach cannot be directly transposed to the quantum case as the system is no longer point-like in phase space. We explain how to adapt our method by an iterative procedure to account for the finite-size quantum distribution in phase space. We show that the resulting driving yields a density matrix close to the synchronized one according to the trace distance. Our method provides an example of fast control of a nonlinear quantum system, and raises the question of the quantum speed limit concept in the presence of nonlinearities.
Self-supervised learning for remote sensing scene classification under the few shot scenario
Authors: Alosaimi Najd , Alhichri Haikel , Bazi Yakoub , Ben Youssef Belgacem , Alajlan Naif 
Published: January 2023

AbstractScene classification is a crucial research problem in remote sensing (RS) that has attracted many researchers recently. It has many challenges due to multiple issues, such as: the complexity of remote sensing scenes, the classes overlapping (as a scene may contain objects that belong to foreign classes), and the difficulty of gaining sufficient labeled scenes. Deep learning (DL) solutions and in particular convolutional neural networks (CNN) are now state-of-the-art solution in RS scene classification; however, CNN models need huge amounts of annotated data, which can be costly and time-consuming. On the other hand, it is relatively easy to acquire large amounts of unlabeled images. Recently, Self-Supervised Learning (SSL) is proposed as a method that can learn from unlabeled images, potentially reducing the need for labeling. In this work, we propose a deep SSL method, called RS-FewShotSSL, for RS scene classification under the few shot scenario when we only have a few (less than 20) labeled scenes per class. Under this scenario, typical DL solutions that fine-tune CNN models, pre-trained on the ImageNet dataset, fail dramatically. In the SSL paradigm, a DL model is pre-trained from scratch during the pretext task using the large amounts of unlabeled scenes. Then, during the main or the so-called downstream task, the model is fine-tuned on the labeled scenes. Our proposed RS-FewShotSSL solution is composed of an online network and a target network both using the EfficientNet-B3 CNN model as a feature encoder backbone. During the pretext task, RS-FewShotSSL learns discriminative features from the unlabeled images using cross-view contrastive learning. Different views are generated from each image using geometric transformations and passed to the online and target networks. Then, the whole model is optimized by minimizing the cross-view distance between the online and target networks. To address the problem of limited computation resources available to us, our proposed method uses a novel DL architecture that can be trained using both high-resolution and low-resolution images. During the pretext task, RS-FewShotSSL is trained using low-resolution images, thereby, allowing for larger batch sizes which significantly boosts the performance of the proposed pipeline on the task of RS classification. In the downstream task, the target network is discarded, and the online network is fine-tuned using the few labeled shots or scenes. Here, we use smaller batches of both high-resolution and low-resolution images. This architecture allows RS-FewshotSSL to benefit from both large batch sizes and full image sizes, thereby learning from the large amounts of unlabeled data in an effective way. We tested RS-FewShotSSL on three RS public datasets, and it demonstrated a significant improvement compared to other state-of-the-art methods such as: SimCLR, MoCo, BYOL and IDSSL.
Protein KIC5 is a novel regulator of artemisinin stress response in the malaria parasite Plasmodium falciparum
Authors: Simmons Caroline F. , Gibbons Justin , Zhang Min , Oberstaller Jenna , Pires Camilla Valente , Casandra Debora , Wang Chengqi , Seyfang Andreas , Otto Thomas D. , Rayner Julian C. , Adams John H. 
Published: January 2023

AbstractArtemisinin combination therapies (ACTs) have led to a significant decrease in Plasmodium falciparum malaria mortality. This progress is now threatened by emerging artemisinin resistance (ART-R) linked originally in SE Asia to polymorphisms in the Kelch propeller protein (K13) and more recently to several other seemingly unrelated genetic mutations. To better understand the parasite response to ART, we are characterizing a P. falciparum mutant with altered sensitivity to ART that was created via piggyBac transposon mutagenesis. The transposon inserted near the putative transcription start site of a gene defined as a “Plasmodium-conserved gene of unknown function,” now functionally linked to K13 as the Kelch13 Interacting Candidate 5 protein (KIC5). Phenotype analysis of the KIC5 mutant during intraerythrocytic asexual development identified transcriptional changes associated with DNA stress response and altered mitochondrial metabolism, linking dysregulation of the KIC5 gene to the parasite’s ability to respond to ART exposure. Through characterization of the KIC5 transcriptome, we hypothesize that this gene may be essential under ART exposure to manage gene expression of the wild-type stress response at early ring stage, thereby providing a better understanding of the parasite’s processes that can alter ART sensitivity.
Exposure of aquatic organisms to natural radionuclides in irrigation drains, Qena, Egypt
Authors: Din K. Salahel , Ahmed N. K. , Abbady A. , Abdallah F. M. 
Published: January 2023

AbstractNatural radioactivity in irrigation drains was measured by gamma spectrometry, and the resulting dose rates received by aquatic organisms were estimated. Irrigation water and sediment samples were collected from 5 irrigation drains located in Qena governorate, south of Egypt. The average activity concentrations (Bq L−1) of 226Ra, 232Th, and 40 K in irrigation water were 0.76 ± 0.06, 0.27 ± 0.02, and 8.14 ± 0.71, while in sediment (Bq kg−1) were 24.46 ± 1.84, 20.72 ± 1.45, and 453.00 ± 28.14, respectively. The total dose rate per aquatic organism ranged from 1.94 × 10–04 µGy h−1 in Mollusc to 7.15 × 10–04 µGy h−1 in phytoplankton. These values are far from the international recommended limit 400 µGy h−1 for chronic exposure to aquatic organisms, and the dose rate screening value of 10 µGy h−1 suggested by ERICA tool. Based on these results, it is unlikely that harmful effects will appear on the considered aquatic organisms due to exposure to natural radioactivity in the studied environment.
Genetic variations in patient with Parry–Romberg syndrome
Authors: Yu Bao-Fu , Dong Li-Ping , Dai Chuan-Chang , Wei Jiao 
Published: January 2023

AbstractParry–Romberg syndrome is a rare craniofacial disorder which is characterized by progressive facial atrophy. The etiology and pathogenesis of the disease are not known. Herein, we report the genetic variants in patient with this disease. A 25-year-old woman was diagnosed with Parry–Romberg syndrome according to her clinical manifestation, which presented with typical progressive unilateral facial soft tissue atrophy. Using peripheral blood samples, Whole exome sequencing (WES) was conducted on this patient and her parents. Variant loci of the genes were validated by Sanger sequencing in her twin sister who had no Parry–Romberg syndrome. Subsequently, we searched the GeneCards®: the Human Gene Database for variant genes, annotated them and analyzed their functions. The results of WES showed that 2 genes (MTOR, DHX37) were mutated, and the variant loci were MTOR: NM_004958.4: exon31: c.4487A>T: p.Q1496L and DHX37: NM_032656.4: exon17: c.2180C>T: p.T727M, respectively. However, the variant loci were also detected in her twin sister by Sanger sequencing. The Human Gene Database for variant genes shows that the two genes may be associated with craniomaxillofacial developmental abnormalities. Although MTOR and DHX37 genes were tested and found to have mutations in patient with Parry–Romberg syndrome, these variants may not directly determine the clinical phenotype. When studying clinical etiology, other factors, such as the environment, should also be taken into account.
Mechanical behavior analysis of fully grouted bolt under axial load
Authors: Liu Xiujun , Ma Zhanguo 
Published: January 2023

AbstractBased on the idea of discretization and the force balance analysis of each mass spring element, a spring-element analysis method for bolt is proposed. By analyzing the mechanical behavior of the bond interface of the fully grouted bolt, three coupling models of the bond interface, the slider model, the spring model and the spring-slider model, are proposed. Using the spring-element method, five load transfer models, namely the slider model, the spring model, the modified spring model, the spring-pulled slider model, and the spring-slider model, were deduced. And get the bolt displacement distribution function, axial force distribution function and shear stress distribution function under each model. The five proposed models are verified, analyzed and discussed by using the pull-out test of the smooth steel bolt and the threaded steel bolt. It is verified by experiments that this study is helpful to comprehensively understand the mechanical behavior of fully grouted bolts under axial load. The proposed spring element analysis method is simple and easy to understand and the results are reasonable.
Oscillation of higher-order canonical delay differential equations: comparison theorems
Authors: Salah Hend , Moaaz Osama , Cesarano Clemente , Elabbasy Elmetwally M 
Published: January 2023

Abstract
In this work, we study the oscillatory properties of a higher-order neutral delay differential equation. By using the principle of comparison with equations of the first order we establish a set of new oscillation criteria for this equation under the canonical condition. Furthermore, the new criteria extend and complement some previous results in the literature. To that end, we compare these criteria by applying them to special cases of the equations under consideration in order to determine which one is the most efficient and least restrictive.
Limits on the cosmic neutrino background
Authors: Bauer Martin , Shergold Jack D. 
Published: January 2023

Abstract
We present the first comprehensive discussion of constraints on the cosmic neutrino
background (CνB) overdensity, including theoretical, experimental and cosmological limits for
a wide range of neutrino masses and temperatures. Additionally, we calculate the sensitivities of
future direct and indirect relic neutrino detection experiments and compare the results with the
existing constraints, extending several previous analyses by taking into account that the CνB
reference frame may not be aligned with that of the Earth. The Pauli exclusion principle strongly
disfavours overdensities ην
≫ 1 at small neutrino masses, but allows for overdensities
ην
≲ 125 at the KATRIN mass bound mν
≃ 0.8 eV. On the other
hand, cosmology strongly favours 0.2 ≲ ην
≲ 3.5 in all scenarios. We find
that direct detection proposals are capable of observing the CνB without a significant
overdensity for neutrino masses mν
≳ 50 meV, but require an overdensity
ην
≳ 3 × 105 outside of this range. We also demonstrate that relic neutrino
detection proposals are sensitive to the helicity composition of the CνB, whilst some may be
able to distinguish between Dirac and Majorana neutrinos.
A Robust RFI Identification Method for Radio Interferometry Based on LightGBM
Authors: Li Weijie , Cao Zhong , Deng Hui , Mei Ying , Chen Linjie , Wang Feng 
Published: January 2023

Abstract
Radio frequency interference is an essential factor affecting the observation data quality of radio telescopes. In constructing the Square Kilometer Array (SKA) radio interferometer, dealing with radio frequency interference (RFI) effectively is always a hot issue in data processing. Traditional identification methods have poor precision or recall, and existing machine-learning-based methods have complicated models and low processing efficiency. We propose a LightGBM identification method based on previous machine-learning research to identify RFI. Based on the data of SKA1-LOW simulation observations, we construct five visibility function data sets, one for modeling and the rest for validation. The experimental results show that the F
2-score reaches 0.9583, and the training and prediction speed are much more efficient than those of convolutional neural networks in a similar recent study. Then, we further investigate the effectiveness of this model in identifying RFI from actual MeerKAT observations. The results show that the overall effectiveness is comparable to tools such as Tfcrop and Rflag, improving over existing methods in identification speed.