bluuebunny/crossref_metadata_2025 huggingface dataset:
Development Characteristics of Coseismic Landslides Induced by the Jishishan Earthquake and Implications for Seismogenic Tectonics
Authors: Huang Yuandong , Xu Chong , He Xiangli , Cheng Jia , Xu Xiwei , Tian Yingying
Published: November 2024

This study provides a comprehensive interpretation and analysis of landslides triggered by the 2023 Jishishan earthquake using remote sensing imagery and GIS technology. A total of 2,643 landslide vector polygons were obtained, and their spatial distribution, scale characteristics, and relationships with environmental factors were thoroughly investigated. The study reveals that the coseismic landslides are primarily concentrated in the hilly and plain areas of the eastern part of the study region. The landslides are predominantly of moderate scale. They are significantly distributed under the following conditions: elevations of 1700-2300 m, slopes of 20-40°, southeast and south aspect slopes, middle slope postion, Paleogene and Neogene stratum, transverse and incline slope structures, within 400 m of rivers, NDVI values of 0.2-0.6, and peak ground acceleration (PGA) of 0.45-0.6g. This study also discusses the seismogenic fault by integrating the aftershocks sequence distribution with existing research findings. The analysis suggests that the distribution characteristics of coseismic landslides support the hypothesis that the seismogenic fault of this earthquake is a NW-SE striking, NE-dipping thrust fault, exhibiting a pronounced "hanging wall effect." The conclusion provides significant insights into understanding the tectonic background of the Jishishan earthquake and the mechanisms underlying secondary hazards. This study not only enriches the database of coseismic landslides in Northwest China but also deepens the understanding of earthquake-triggered landslide mechanisms and their implications for seismogenic structures. It is of great significance for enhancing earthquake hazard risk assessment and emergency response capabilities.
Platelet indices and idiopathic pulmonary fibrosis: a multivariable mendelian randomization study
Authors: Zheng Yu , Liang Yuanyu , Zhu Lingyun , Li Changhui , Zang Ningzi , Wang Tianjiao , Pang Lijian , Lv Xiaodong , Liu Yongming
Published: November 2024

Background
The platelet indices can be used to reflect platelet activation. Previous studies have found that platelet activation plays an important role in thrombotic diseases. However, it is still unclear whether platelet activation induces the progression of IPF hypercoagulability. Therefore, we conducted a Mendelian randomization (MR) study to compare the causal relationship between gene determined platelet indices and IPF levels.
Methods
Single nucleotide polymorphisms (SNPs) were used as instrumental variables in relation to platelet count (PLT), hematocrit (PCT), mean platelet volume (MPV), platelet distribution width (PDW), and blood pressure IPF (p < 5 ×10− 8) from the UK Biobank. In bidirectional univariate MR analysis, inverse variance weighting (IVW), MR Egger, and weighted median methods are applied to obtain estimates of individuals. In addition, heterogeneity and sensitivity analyses were conducted to test the pleiotropy of the effects. Finally, a multivariate MR analysis was conducted to elucidate the comparative effects of the four types of platelets.
Results
In MR, we found no causal relationship between PLT, MPV, PDW, and PCT levels predicted by host genetics and IPF. In MVMR, the relationship between PLT, MPV, PCT and IPF is significant.
Conclusion
These findings suggest that PLT, MPV, and PCT as overall features may jointly participate in platelet activation. The increase in platelet activation and aggregation may be involved in the pathogenesis of IPF, which may provide insights for evaluating microvascular thrombosis events in IPF patients.
The processing for label noise based on attribute reduction and two-step method
Authors: Wu Xingyu , Zhu Ping
Published: November 2024

Classification is a mainstream task in machine learning. To achieve good classification results, there are many aspects to consider. Among them, label noise is the most direct and fundamental problem. Nowadays research targets the processing of label noise in numerous aspects, including correction, filtering and enhanced robustness methods. All these methods have improved the classification accuracy to some extent. However, the above studies consider only one approach to label noise, such as solely focusing on filtering or exclusively on correction. Label noise is complex and it is singular to consider only one method to deal with it. For example, contaminated data in a certain class and noise belonging to this class, both belong to the label noise problems, but with completely different distributions and treatments. This requires us to discuss the situations separately and to propose different processes. In this paper, we take this into account and propose a noise processing method that combines revision and filtration (RF). The RF method can follow the different distributions of label noise and perform targeted processes, which is more effective and comprehensive. It can maintain the original data distribution and remove noise as much as possible. On the other hand, high-dimensional datasets are encountered when dealing with label noise. The attribute values of the dataset will be abnormal due to the presence of label noise. Therefore, we suggest an attribute reduction method for the case when label noise exists. The advantage is that it not only removes redundant attributes, but also eliminates attributes interfered with by noise, which is suitable for high-dimensional data with label noise. Experiments prove that our proposed RF algorithm is effective among three classifiers with multiple comparison algorithms. Performing attribute reduction also improves classification accuracy significantly.
Industrial 300mm wafer processed spin qubits in natural silicon/silicon-germanium
Authors: Koch Thomas , Godfrin Clement , Adam Viktor , Ferrero Julian , Schroller Daniel , Glaeser Noah , Kubicek Stefan , Li Ruoyu , Loo Roger , Massar Shana , Simion George , Wan Danny , De Greve Kristiaan , Wernsdorfer Wolfgang
Published: November 2024

The realisation of an universal quantum computer will require the operation of thousands to millions of qubits. The possibility of using existing industrial semiconductor fabrication techniques and infrastructure for up-scaling and reproducibility makes silicon based spin qubits one of the most promising platforms to achieve this goal. The implementation of the up to now largest semiconductor based quantum processor was realized in a silicon/silicon-germanium heterostructure known for its low charge noise, long qubit coherence times and fast driving speeds, but the high structural complexity creates challenges for industrial implementations. Here we demonstrate quantum dots hosted in a natural Si/SiGe heterostructure fully fabricated by an industrial 300mm semiconductor wafer process line from heterostructure growth to Co micromagnet monolithic integration. We report charge noise values below 2μeV/√Hz, spin relaxation times of over 1s and coherence times T2* and T2H of 1μs and 50μs respectively, for quantum wells grown using natural silicon. Further, we achieve Rabi frequencies up to 5MHz and single qubit gate fidelities above 99%. In addition to scalability, the high reproducibility of the 300mm processes enables the deterministic study of qubit metric dependencies on process parameters, which is essential for optimising qubit quality.
18 F-FDG and 18 F-AlF-NOTA-octreotide PET/CT in liver infection lesions after operation of duodenal neuroendocrine tumor
Authors: Xu Honghao , Yao Shuzhan , Xu Shuaishuai
Published: November 2024

Background: Neuroendocrine neoplasms (NEN) arise from the diffuse neuroendocrine cells and most frequently occur in the digestive system. Molecular imaging can allow whole body neoplasm characterization, with sites of well and poorly differentiated disease demonstrated at somatostatin analogs (SSA) PET/CT and FDG PET/CT, respectively. Recently, several studies have confirmed the diagnostic accuracy of dual tracer PET/CT using SSA and FDG.
Case presentation: A 63-year-old male patient with duodenal neuroendocrine tumor (NET), Ki-67 proliferation index of 10%, was referred for recurrence detection with dual tracer PET/CT using 18F-FDG and 18F-AlF-NOTA-octreotide (18F-OC) six months after operation. Multiple slightly low-density lesions, 18F-FDG-positive and 18F-OC-negative, were found in the liver. The hypothesis of metastatic liver NET was made. Subsequent biopsy revealed inflammatory cell infiltration and fibroblast-like cell proliferation in the stroma, and no atypical cell was observed.
Conclusions: This case report underscores the importance of understanding the biology and mechanism of both glycometabolism and SSA uptake when interpreting 18F-FDG and SSA PET/CT in NEN.
Satb2 and Nr4a2 are required for the differentiation of cortical layer 6b
Authors: Song Ning-Ning , Zhao Li , Tao Yun-Chao , Hu Ling , Liu Xi-Yue , Zhang Qiong , Zhang Lei , Ding Yu-Qiang
Published: November 2024

Cortical layer 6 is divided into two sublayers, and layer 6b is situated above the white matter with distinct architecture from layer 6a. Layer 6b arises from the subplate and contains the earliest born neurons in the development of cerebral cortex. Although great progress has been made in understanding the cortical morphogenesis, there is a dearth of knowledge regarding the molecular mechanisms governing the development of layer 6b neurons. Here we report that transcription factor special AT-rich binding protein 2 (Satb2) and nuclear receptor subfamily 4 group A member 2 (Nr4a2) are required for the normal differentiation layer 6b neurons. Upon conditional deletion of Satb2 in the cortex (Satb2Emx1 CKO) or selectively inactivation of Satb2 in layer 6b neurons only (Satb2Nr4a2CreER CKO), the expressions of layer 6b-specific genes (i.e., Ctgf, Cplx3, Trh and Tnmd) were significantly reduced, whereas that of Nr4a2 was dramatically increased, underscoring that Satb2 is involved in the differentiation of layer 6b neurons in a cell-autonomous manner. On the other hand, when Nr4a2 was deleted in the cortex, the expressions of Trh and Tnmd were upregulated with unchanged expression of Ctgf and Cplx3. Notably, the defective differentiation resulting from the deletion of Satb2 remained in Satb2/Nr4a2 double CKO mice. In summary, our findings indicated that both Satb2 and Nr4a2 are required for the differentiation of layer 6b neurons possibly via different pathways.
Research Coordinators’ Perspectives on Recruitment of Minoritized People with Cystic Fibrosis into Clinical Trials
Authors: Milinic Tijana , Burdis Nora , Gill Eliana , Burks Patricia , Jarosz Mary Elizabeth , Hoffman Amy J. , Kapnadak Siddhartha G. , Ramos Kathleen J. , Goss Christopher H.
Published: November 2024

Background
Clinical trials in cystic fibrosis (CF) disproportionately over-represent non-Hispanic, White individuals. Barriers and facilitators to enrolling racially and ethnically minoritized individuals with CF are not well understood. This study explored research coordinator (RC) perspectives on recruitment and enrollment of minoritized people with CF (PwCF) into clinical trials.
Methods
A cross-sectional survey was administered to RCs in the CF Therapeutics Development Network (CF TDN), eliciting perceived barriers and facilitators to inclusion of minoritized PwCF in clinical research. Respondents were categorized based on their self-reported experience discussing and successfully enrolling minoritized PwCF into clinical trials.
Results
Among 48 respondents, the majority (n = 31, 64%) had little to no experience discussing CF clinical trials with minoritized PwCF. Respondents who had a moderate or great deal of experience identified that having a trusted clinical team member first introduce the study to PwCF as the most common strategy for recruitment. Experienced respondents also identified the importance of having team members who speak the same language or are the same culture as the minoritized PwCF. Respondents who had little to no experience successfully enrolling minoritized PwCF into clinical trials cited low numbers of minoritized patients at their study center as an important consideration. All respondents emphasized language barriers in enrollment including need for adequate translation services and printed materials in the PwCF’s primary language.
Conclusion
Our study identified modifiable barriers that may be addressed at the level of trial design and study center.
Genotypic variations in leaf pigments, metabolites content, and antioxidant activities of chickpea (Cicer arietinum L.) plants under salinity stress conditions
Authors: Flayyih Hydar , Nezami Ahmad , Nabati Jafar , Ahmadi Mohammad Javad , Kafi Mohammad
Published: November 2024

Chickpea (Cicer arietinum L.) with high nutritional value plays a significant role in the growing world population nutrition. The yield and productivity of salinity-sensitive legumes such as chickpeas are adversely affected by salinity stress. However, because of the high genetic variability of chickpea plants, the performance of some genotypes was evaluated under salinity stress conditions (0.5, 6, and 9 dS.m-1) to screen the more salinity tolerant. The results revealed that MCC12, MCC22, MCC27, MCC65, MCC72, MCC77 and MCC108 genotypes at 6 dS.m-1 and MCC27, MCC65, MCC72, MCC77 and MCC52 genotypes at 9 dS.m-1 showed > 70% of plant survival. The stepwise regression results showed that leaf photosynthetic pigments and soluble carbohydrates content, peroxidase (POX) and ascorbate peroxidase (APX) activities, and plant dry weight confirmed the survival variation. The principal component analysis (PCA) showed that at 6 dS.m-1, proline and shoot dry weight, and at 9 dS.m-1, POX activity and shoot dry weight showed the highest values of the first and second components, respectively. The highest and lowest stress tolerance index (STI) at 6 dS.m-1 were observed in MCC28 and MCC65 genotypes, respectively. Furthermore, at 9 dS.m-1, the highest and the lowest STI were observed in MCC27 and MCC52 genotypes, respectively. High correlations were observed between plant survival with photosynthetic pigments and DPPH. Generally, higher leaf relative water content (RWC), leaf chlorophyll and metabolites content, and antioxidant activities helped chickpea genotypes maintain their survival and continued growth under salt-stress conditions.
LISBET: a machine learning model for the automatic segmentation of social behavior motifs
Authors: Bellone Camilla , Chindemi Giuseppe , Girard Benoit
Published: November 2024

Social behavior is crucial for survival in many animal species, and a heavily investigated research subject. Current analysis methods generally rely on measuring animal interaction time or annotating predefined behaviors. However, these approaches are time consuming, human biased, and can fail to capture subtle behaviors.
Here we introduce LISBET (LISBET Is a Social BEhavior Transformer), a machine learning model for detecting and segmenting social interactions. Using self-supervised learning on body tracking data, our model eliminates the need for extensive human annotation.
We tested LISBET in three scenarios across multiple datasets in mice: supervised behavior classification, unsupervised motifs segmentation, and unsupervised animal phenotyping. Additionally, in vivo electrophysiology revealed distinct neural signatures in the Ventral Tegmental Area corresponding to motifs identified by our model. In summary, LISBET automates data annotation and reduces human bias in social behavior research, offering a promising approach to enhance our understanding of behavior and its neural correlates.
WITHDRAWN: From myometrium to fibrosis: the underlying mechanism of endometrial injury in intrauterine adhesions
Authors: Xu Xiaotong , Guo Kaixuan , Zhao Peng , Zhang Xuemei , Zhao Pan , Sun Xianghang , Zhang Mingle , Tian Yanpeng , Zheng Jiahua , Huang Xianghua
Published: November 2024

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Lyophilized preparation of root endophyte fungus Piriformospora indica with carbon-based nanomaterial (carbon dots) successfully colonizes the plant host, Cicer arietinum
Authors: Yadav Bindu , Mourya Pallavi , SIngh Rajeshwar Pratap , Smriti  , Solanki Pratima , Johri Atul Kumar , Dua Meenakshi
Published: November 2024

Root endophyte fungus Piriformospora indica can be axenically cultivable, is easily obtained in pure cultures in the laboratory, and therefore, can be developed as a biofertilizer for bioaugmentation. In this study, an effort towards sustainable organic agriculture, we have made two completely eco-friendly, biogenic and biocompatible lyophilized formulations of P. indica, one, without carbon dots and second, with carbon dots. Scanning Electron Microscopy (SEM) observations, viability assays and colonization efficiency of both the formulations revealed that lyophilization does not in any way alter either the morphology, growth or colonization ability of the endophyte. The formulations were also tested for impact on growth of Cicer arietinum plants in experimental set up. The plants were analysed for changes in dry weight, shoot length, root length and branch numbers. While the dry weight increased by a maximum of 1.9-fold; average shoot length increased by 1.4-fold; average root length by 1.7-fold; and number of branches by 1.4-fold, when compared to plants grown without any P. indica. These increases were found to be statistically significant. We identify this work as a significant step towards optimizations and production of this formulation on a larger scale. We also perceive this attempt as commitment towards United Nations SDGs 2,3 and 13.
The Association between Self-Esteem, Physical Activity, and Menopausal Symptoms in Postmenopausal Women: A Cross-Sectional Study
Authors: Ramezanzadeh Faezeh , Hamidia Angela , Pourhadi Samaneh , Shirafkan Hoda
Published: November 2024

Background
Menopause is a natural transition often accompanied by various physical and psychological symptoms. This study examines the associations between self-esteem, physical activity levels, and menopausal symptoms in postmenopausal women.
Methods
A cross-sectional design recruited 124 women aged 45–60 years who were postmenopausal. Data on sociodemographics, self-esteem, physical activity, and menopause-specific quality of life were collected using special questionnaires. Correlations and multivariable linear regression analyses explored relationships.
Results
Self-esteem scores showed significant negative correlations with psychosocial (p = 0.000), physical (p = 0.000), and to a lesser extent, vasomotor symptoms (p = 0.002). While physical activity levels did not exhibit a significant association with overall menopause-specific quality of life scores, a trend towards an association with psychosocial symptoms (p = 0.130) was observed, warranting further investigation. Multivariable regression analysis identified self-esteem as a significant predictor for various symptoms, with higher self-esteem linked to reduced symptom severity.
Conclusions
This study suggests a positive association between self-esteem and reduced menopausal symptoms. While a definitive link between physical activity and symptom burden wasn't established, a potential connection with psychosocial symptoms warrants further exploration. Future research with larger, more diverse populations is recommended. Interventions focusing on self-esteem enhancement may be beneficial for improving quality of life in postmenopausal women.
Perceptions of Dementia and Their Implications on Disease Management in Egypt: A Perspective
Authors: Heikal Shimaa A. , Moghith Aalaa A. , Salama Mohamed
Published: November 2024

The social stigma and negative perceptions about Dementia are major problems that hinder disease management. Yet, the scarcity of resources that investigate the situation in Egypt limits our understanding of the role of stigma and its implications on the disease in the country. In this study, we explored the perceptions of Dementia in Egypt by investigating the perspectives of experts and healthcare providers to understand the implications on disease management. We conducted 24 in-depth interviews with experts in different fields who are aware of the Egyptian context. The findings reveal that social stigma significantly hinders early diagnosis and effective management of Dementia. Patients often avoid seeking medical help due to the fear of social stigma, leading to late-stage diagnosis and diminished treatment efficacy. The study emphasizes the need for community awareness and policy interventions to provide protective measures for securing patients and decreasing disease misconceptions for improved Dementia care in Egypt.
Seismic back slope effects of slopes: a case from Yinxing village of China triggered by the May 12th, 2008, Wenchuan earthquake
Authors: Zhang Lei , Wang Yunsheng , Tan Zhihua , Fan Yanyun , Jiang Fasen , Mi Jian , Xu Hongbiao
Published: November 2024

One of the most severe geological hazards is caused by earthquakes. Revealing the rules about the geo-hazards triggered by earthquake plays an important role in managing them. Here, in-situ research is thoroughly carried out in the study area and neighboring area. Combining in-situ research with numerical simulating by FLAC3D, the effect is studied further. Our research suggests that: 1) The in-situ investigation shows that, the lithology and joint development of the rock mass on the front and back slopes are almost identical, and the back slope effect is obvious. 2) The back slope has a larger shear failure zone than the front slope. The model's monitoring results for acceleration, velocity, displacement and stress also reveal a sizable back slope amplification effect. 3) Acceleration and velocity both follow the same distribution rule for their amplification coefficients, which is that it tends to rise from the lowest part and peak at the middle-lower part. Stress and displacement’s strongest amplification effect is visible in their y and z direction components.
Blade configuration and depth ratio impact on undershot waterwheel in compact irrigation for sustainable off-grid electrification
Authors: Sari Dewi Puspita , Adanta Dendy , Syofii Imam , Fudholi Ahmad , Zawawi Fazila Mohd , Mizan Muhammad
Published: November 2024

Undershot waterwheel turbines (USWWs) are promising renewable energy sources for off-grid electrification in developing countries, especially for low-head applications. This study aimed to compare the performance of wide-blade and large-diameter USWW designs for compact irrigation infrastructure. Two USWW configurations were tested: one with a large diameter and one with a wide blade. Numerical simulations using computational fluid dynamics (CFD) were performed and validated with experimental data. The results showed that the wide-blade design achieved significantly higher torque and power output than the large-diameter design, demonstrating the importance of blade configuration for efficient energy transfer. The study also developed an empirical equation for the relationship between the blade depth ratio and efficiency, highlighting the impact of this parameter on USWW performance. Finally, the investigation of tip speed ratio and hydrostatic force provided valuable insights into the hydraulic behavior of USWWs and their optimal operation. This study offers valuable insights for designing efficient and environmentally friendly USWWs for remote areas, paving the way for broader adoption of this renewable energy technology.
The relationship between high intensity activities and kidney stone: A cross-sectional survey of NHANES
Authors: Nong Fengwei , Liang Zhengfang , Chen Runmin , Huang Yongping , Wang Jie
Published: November 2024

Background
Kidney stone disease (KSD) occurs in a wide range of ages and is influenced by multiple factors. Early detection and elimination of risk factors for KSD can effectively reduce the incidence of KSD.
Methods
In this research, we included KSD participants from the National Health and Nutrition Examination Survey (NHANES) database from 2007 to 2015. Baseline characteristics of the participants were investigated using Student's t-tests and chi-square tests. Subsequently, the relationship between high intensity activities (HIA) and KSD was investigated through multifactor glm regression modeling. In addition, the linear relationship between them was explored by smoothing curves. Finally, the predictive performance of HIA on KSD was explored based on receiver operating characteristic (ROC) curves.
Results
At first, 6,642 subjects were finally recruited for this study. The baseline statistical table showed that the exposure factor (time spent in HIA) was chosen to have a significant differentiation for KSD. In addition, HIA was significantly associated with KSD in all three models, with OR greater than 1 and P < 0.05. The smoothed curves showed that short periods of HIA did not increase the risk of KSD, but over a certain period of time greatly increased the risk of KSD. Stratified analysis results showed that exposure factors and race, educational status, and gout were significantly associated with KSD in Model 3. Eventually, ROC curve indicated the prediction for HIA to KSD was relatively accurate.
Conclusion
This study revealed a link between HIA and KSD, with HIA over a certain period of time greatly increasing the risk of KSD.
Exponentially-enhanced quantum sensing with many-body phase transitions
Authors: Sarkar Saubhik , Bayat Abolfazl , Bose Sougato , Ghosh Roopayan
Published: November 2024

Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that many-body systems featuring first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, $p$-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis.
Machine learning wildfire susceptibility mapping for Germany
Authors: Thies Boris
Published: November 2024

Wildfires present a significant threat to ecosystems and human life, particularly as
global climate change amplifies the likelihood of extreme fire events. This study
develops a machine learning-based wildfire susceptibility model for Germany, using
data between 2003 and 2023. The primary goal is to identify the dominant wildfire
predictors and create monthly susceptibility maps. A Random Forest (RF) algorithm
was trained on remote sensing data and a comprehensive set of predictors, including
meteorological, terrain, and land cover variables. The results indicate that surface air
pressure, elevation, vegetation health, and proximity to urban areas are the most
important factors in predicting fire susceptibility. The model achieved 89% accuracy,
demonstrating the effectiveness of data-driven approaches in wildfire risk modeling.
The monthly susceptibility map for July 2022 highlights northeastern Germany as
particularly vulnerable to fire outbreaks. The results offer valuable insights for targeted
wildfire prevention and resource allocation, emphasizing the importance of both
temporal and spatial dimensions in managing wildfire risks.
Increasing matrix metalloproteinase-2 (MMP-2) activity by treatment of ovine cervical explants with a long-acting analogue of oxytocin (Carbetocin) at the expected time of artificial insemination.
Authors: Rodríguez-Piñón Marcelo , García-Barcelo Gonzalo
Published: November 2024

The aim was to study the effect of long-acting analogue of oxytocin (Carbetocin, Cb) on cervical collagenolysis of MAP-eCG synchronized ewes. At the expected time of artificial insemination, five ewes were slaughtered (n = 5) and their cervical explants (100–200 mg) were incubated during 12 h with MEM supplemented with 0, 8, 16, 32 and 64 ng/mL of Cb. Activities of activated (A) and latent (L) forms of matrix metalloproteinases-2 and − 9 (MMP-2 and MMP-9, respectively) in the supernatant were determined by a SDS-PAGE zymography and PGE2 concentration immunoassay. Data were analyzed by Welch's ANOVA test. The MMP-9 activity was detected weakly and sporadically. The activity of A MMP-2 was higher in 32 ng/mL Cb treated samples than in untreated samples (P < 0.03). The activity of L MMP-2 tended to be higher in 32 ng/mL than in 8 ng/mL Cb treated samples (P = 0.0784). The A/L ratio of MMP-2 tended to be higher in 64 ng/mL Cb treated samples than in untreated samples (P = 0.0955). PGE2 concentration was not affected by Cb dose. The data show that Cb treatment of cervical explants induces an increase in MMP-2 activity by PGE2-independent mechanisms, suggesting Cb as a potential inducer of cervical dilation in sheep.
Endeavoring the First Lanthanide–Carbon Triple-Bond in Fullerene Cage
Authors: Li Jun , Chen Ning , Jiang Hongjie , Zhao Jing , Meng Qingyu , Zhao Xiao-Kun , Guo Min , Hu Han-Shi
Published: November 2024

Metal–ligand multiple bonds are intriguing in coordination and organometallic chemistry. However, lanthanide–carbon multiple bonds are extremely difficult to form. Despite decades of effort, isolable complexes containing lanthanide–carbon double bonds with terminal methyl carbene (= CH2) and lanthanide–carbon triple bonds have never been found. Here, we report the successful synthesis of an unprecedented lanthanide–carbon triple-bonded compound with cerium-carbide [Ce≡C − Sc2] cluster encapsulated inside a C80 fullerene cage. The molecular structure of CeCSc2@C80 and the nature of the Ce≡C triple bond are characterized through X-ray crystallography, spectroscopic analyses, and quantum chemical study, revealing a very short Ce≡C distance of 1.978(8) Å. Chemical bonding analysis suggests that the formation of the Ce≡C bond primarily arises from the stronger bonding affinity between carbon and cerium compared to scandium inside the encapsulated cluster. The fullerene cage plays a crucial role in stabilizing and protecting this trimetallic carbide cluster with a Ce≡C triple bond.
Inhibition of Growth and Biofilm Formation in Staphylococcus aureus by LLY-507
Authors: Tang Yuanyuan , Xu Zhichao , Lai Yingying , Zhang Jialang , Zheng Jinxin , Wen Zewen , Yu Zhijian , Chen Zhong , Han Shiqing , Li Peiyu , Hou Tieying
Published: November 2024

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) as a prevalent antibiotic-resistant pathogen underscores the urgent need for novel antibacterial agents. This study investigates the potential of LLY-507 against gram-positive bacteria, particularly S. aureus, focusing on its antibacterial and anti-biofilm properties. Here, our data exhibited favorable antibacterial activity of LLY-507 with MIC50 and MIC90 values of 25 µM against S. aureus. Additionally, LLY-507 at sub-MIC concentrations effectively reduced the planktonic growth and biofilm formation of S. aureus. Proteomic analysis of S. aureus treated with LLY-507 revealed the classification of the functional proteins with significant expression level alterations in bacterial metabolism, particularly amino acid biosynthesis. Furthermore, we demonstrated the disruption of S. aureus cell integrity by LLY-507 through membrane permeability assays and direct binding experiments between LLY-507 and cardiolipin. Lastly, the effectiveness of LLY-507 was proven in vivo using the G. mellonella infection model. Overall, these findings highlight the promising antibacterial and anti-biofilm activities of LLY-507 against S. aureus and provide insights into its mechanism of action, implicating its potential as a lead compound for developing novel antibacterial agents targeting Gram-positive bacteria.
Assessing Prevalence and Risk Factors for REM Sleep Behavior Disorder Among Patients with Inflammatory Bowel Disease
Authors: Reddy Vijaya , Chen Zihan , Dewain Sohail , Joo Michelle , Bellomo Maya , Renkl Erika , Patel Sapna , Rivera Emily , Weisbein Laura , Ali Ahmed , Kappelman Michael D. , Koo Brian B.
Published: November 2024

REM Sleep Behavior Disorder (RBD) and Inflammatory Bowel Disease (IBD) are associated with Parkinson’s Disease. We hypothesized that RBD prevalence is greater in IBD than controls. 158 controls and 462 IBD patients completed IBD-related history questionnaires and the RBD Single-Question Screen (RBD1Q) and RBD-Screening Questionnaire assessing RBD. RBD prevalence was compared using chi-squared testing. Logistic regression explored IBD-related factors associated with RBD (RBD1Q), adjusting for age, sex, race. RBD prevalence was higher in IBD (14.1% vs. 5.1%; p = 0.002). Differential RBD prevalence was driven by changes in women; 13.3% IBD and 1.0% controls had RBD. For men, 16.0% IBD and 11.9% controls had RBD. IBD was associated with > 3-fold RBD likelihood (OR = 3.18, 95% CI [1.48,6.82], p = 0.003). Men with RBD and IBD were more likely than IBD males without RBD to have IBD-related hospitalizations (95.2% vs. 63.0%; p = 0.004). RBD is more prevalent in IBD than controls and is associated with severe IBD.
Examining Human Coronavirus Spike Protein Sequences and Evolutionary Distances May Give Insights into the Origins of SARS-CoV-2.
Authors: Wainwright Felicity , Misirli Goksel , Andras Peter
Published: November 2024

Introduction Since the emergence of the human coronavirus SARS-CoV-2, and the consequent COVID-19 pandemic, the nature of the virus’ origin has been hotly debated. The possibility that it was artificially engineered in a laboratory setting has been considered, as has the possibility that it simply evolved naturally from existing known human coronaviruses. In this paper, we provide a comparative approach that involves examining the evolutionary distance between all known human coronaviruses by using genetic sequences of their spike proteins and proteases and determining where SARS-CoV-2 fits within this evolutionary picture to provide insights into the origins of the virus. A similar distance-based approach has been applied to related organisms using different protein sequence data to test if a similar trend exists. Materials and Methods We utilised existing sequence data (previously deposited in online databases) of the seven known human coronavirus spike proteins, then mapped them against each other in RStudio using the BLOSUM62 Substitution Matrix to determine the genetic similarity between the spike proteins and ascertain whether the similarity scores for SARS-CoV-2 were within the expected range based upon values for the other coronaviruses. The similarity scores were normalised and then a frequency distribution was obtained. We then repeated this process for selected influenza neuraminidases and norovirus capsid proteins respectively, to compare distribution patterns across the three virus types. Finally, the frequency distribution scores were analysed using a Kolmogorov-Smirnov test. The process detailed above was then repeated in order to compare genetic similarity between human coronavirus protease sequences; proteases are essential for viral replication. We examined both cysteine proteases encoded within the coronavirus genome - the main protease (3CLpro) and the papain-like protease (PLpro). Results By implementing a Kolmogorov-Smirnov test, we demonstrate primarily that the frequency distribution for SARS-CoV-2 is not significantly different to that of the other six known human coronaviruses (D = 0.4, p-value = 0.4195). We also demonstrate that the frequency distributions for coronavirus spike proteins, influenza neuraminidases, and norovirus capsid proteins are all significantly different, demonstrating that each virus type has followed a different evolutionary pattern. Conclusions Based on the similarity scores of SARS-CoV-2 upon comparing spike protein sequences to those of the other six known human coronaviruses, the results suggest that SARS-CoV-2 is sufficiently similar to the other coronaviruses to indicate that it evolved naturally, as opposed to arising due to laboratory manipulation. This supports existing hypotheses theorising that SARS-CoV-2 developed naturally. Our results also show that the different virus types follow clearly different evolutionary paths.
Comparative Outcomes of Intralesional Collagenase Injection for Peyronie's Disease: Acute vs. Stable Phase Treatment
Authors: KUCHAKULLA MANISH , Bhambhvani Hriday , Wald Gal , Basourakos Spyridon , Lewicki Patrick , Gal Jonathan , Kashanian James
Published: November 2024

Collagenase clostridium histolyticum (CCH) was FDA-approved for treating Peyronie’s Disease, demonstrating significant curvature improvement in patients during the stable phase of disease. In this study, we aimed to assess the safety and effectiveness of intralesional CCH injections during the acute phase of Peyronie's Disease. We retrospectively evaluated all men who received intralesional collagenase for Peyronie’s Disease at a single academic institution between July 2017 and January 2023, focusing on patient demographics, comorbidities, pre- and post-treatment curvature, and adverse events. Patients in the acute phase had symptoms for ≤ 12 months with penile pain and/or curvature changes. A total of 100 patients treated with intralesional CCH injections for Peyronie's disease were included. Percent change in penile curvature was not significantly different between acute and stable phases (37.02% vs. 34.01%, p = 0.57). A total of 55 patients (76%) in the stable phase and 23 patients (82%) in the active phase showed improvement in curvature, with no significant differences in curvature outcomes or adverse events between the two groups. This study contributes to the growing evidence that intralesional collagenase treatment during the acute phase of Peyronie's disease is safe and effective.
Fast Noisy Long Read Alignment with Multi-Level Parallelism
Authors: Xia Zeyu , Yang Canqun , Peng Chenchen , Guo Yifei , Guo Yufei , Tang Tao , Cui Yingbo
Published: November 2024

Background:
The advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment tools inapplicable. Additionally, a single CPU's performance bottleneck restricts the effectiveness of alignment algorithms for SMRT sequencing.
Methods:
To address these challenges, we introduce ParaHAT, a parallel alignment algorithm for noisy long reads. ParaHAT utilizes vector-level, thread-level, process-level, and heterogeneous parallelism. We redesign the dynamic programming matrices layouts to eliminate data dependency in the base-level alignment, enabling effective vectorization. We further enhance computational speed through heterogeneous parallel technology and implement the algorithm for multi-node computing using MPI, overcoming the computational limits of a single node.
Conclusion:
Performance evaluations show that ParaHAT got a 5.39x speedup in base-level alignment, with a parallel acceleration ratio and weak scalability metric of 94.61 and 98.98% on 128 nodes, respectively.
Integrating Multiple Slack Bus Operations and Metaheuristic Techniques for Power Flow Optimization
Authors: Dahal Swaechchha , Hegglid Gunne John , Nøland Jonas Kristiansen , Chhetri Bhupendra Bimal , Mishra Sambeet , Øyvang Thomas
Published: November 2024

The increasing complexity of modern energy grids amplifies the importance of realistic power flow studies in power system analysis. This study implements a Multiple Slack Bus Operation (MSO) framework to enhance the realism and efficiency of optimal power flow (OPF) analysis. This paper introduces a comparative evaluation of three metaheuristic algorithms—Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO)—within the MSO framework. The algorithms are assessed based on their effectiveness in system loss minimization, line loading optimization, generator voltage angle adjustment, and generation distribution changes. Utilizing the Reduced Nordic 44 model and IEEE benchmark test systems at various load conditions, the findings reveal that the GWO algorithm, when integrated with the MSO framework, achieves the most significant reduction in total system losses. Specifically, the implementation of MSO alone reduced system losses by 5%, and its combination with GWO led to an additional 8.3% decrease. This study investigates the application of metaheuristic algorithms within a multiple slack bus context, highlighting their potential to enhance power network efficiency and suggesting broader applications for future power flow optimization strategies.
Deep Rank to Match Model via Multi-task Learning for Online Large-scale Recommender System
Authors: Ding XiaoPing , Jiang Yang , Xu JiaLi , Fu GuiJie , Li Xiang
Published: November 2024

Large-scale recommender systems consist of two stages: matching and ranking. The matching stage retrieves candidate items relevant to user interests, while the ranking stage predicts the probabilities of user interactions with these items. Traditional matching models retrieve items with high relevance but not necessarily good business indicators like CTR. Integrating the ranking task into the matching stage using multi-task learning (MTL) addresses this issue. Challenges include dealing with the inconsistency of sample space and training objectives between matching and ranking, and building an MTL framework that meets computational cost requirements. We propose the Deep Rank to Match via Multi-Task Learning (DRMMTL) model to tackle these challenges. DRMMTL introduces the ranking task into the matching stage and uses a dual-tower structure to ensure computational efficiency and consistency of final scores. Offline evaluation on real-world datasets and online A/B tests validate DRMMTL's superiority, showing significant performance improvements over traditional matching models. DRMMTL has been successfully deployed on our e-commerce recommendation platform, handling major online traffic.
A Deep Learning Approach for Detection of Object-Based Forgery in Videos Using Motion Residuals Footprints
Authors: Raj Mrinal , Bakas Jamimamul , Jha Ashutosh
Published: November 2024

Nowadays, with the rapidly evolving digital technology, mul-timedia content has a significant influence on our day-to-day lives. Videocontent plays a crucial role in many cases, especially in the legal indus-try. For example, CCTV-captured videos are now accepted as prime evi-dence of any crime scene in a courtroom. However, surveillance videos canbe easily manipulated using publicly available image and video process-ing tools. This paper introduces a deep learning-based model to detectobject-based forgery in surveillance videos. In this research, we exploitmotion residuals to capture the footprints generated due to forgery oper-ations. A VGG-19 network is used to learn the extracted motion residualsand detect the forged frames. The proposed model was tested for effec-tiveness through a comprehensive experiment, achieving an F1-score of98.88%.
Fungal lignocellulolytic enzymes: an in silico and full factorial design approach
Authors: Candeo Esteffany de Souza , Scheufele Fabiano Bisinella , Pena Aline de Cassia Campos , Dequigiovanni Gabriel , Linde Giani Andrea , Mata Gerardo , Colauto Nelson Barros , Schaker Patricia Dayane Carvalho
Published: November 2024

Efficient degradation of lignocellulosic biomass is key for the production of value-added products, contributing to sustainable and renewable solutions. This study employs a two-step approach to evaluate lignocellulolytic enzymes of Ceratocystis paradoxa, Colletotrichum falcatum, and Sporisorium scitamineum. First, an in silico genomic analysis was conducted to predict the potential enzyme groups produced by these fungi. Second, a 2³ full factorial design of solid-state cultivation was employed to investigate the cultivation conditions that optimize enzyme activity. In silico analysis of phytopathogen genomes identified proteins with the potential for biomass degradation. Cellulase and phenoloxidase activities were assessed in culture medium and solid-state cultivation. A 2³ full factorial design was employed for solid-state cultivation to evaluate the cellulose, endoglucanase, and laccase activities. In silico analysis shows that C. falcatum has the most diverse enzyme set for lignocellulosic biomass degradation. In vitro assays corroborate this, demonstrating that C. falcatum produces the highest enzyme quantities, except for cellulase, where C. paradoxa outperforms it. Both C. paradoxa and C. falcatum exhibit cellulase and phenoloxidase activities, but only C. falcatum shows laccase activity. Most favorable enzyme production in solid-state cultivation occurred with 85–95 g 100 g− 1 bagasse moisture and 5 g 100 g− 1 yeast extract, with four-day cultivation period needed for cellulase and endoglucanase in C. paradoxa and 12 days for endoglucanase and laccase in C. falcatum. The in silico and in vitro assays demonstrated that C. falcatum can produce a diverse enzyme set, including laccase, cellulase, and endoglucanase, making it a promising candidate for enzymatic industrial applications.
Analog performance investigation of 10 nm Junctionless GAA FETs using Machine learning methods and deep learning analysis
Authors: Ouchen R. , Berghout T. , Djeffal F. , Ferhati H.
Published: November 2024

With the continuous downscaling of analog CMOS-based circuits, the sensitivity of nanoelectronic devices to design parameter variations has significantly increased. In this paper, we introduce a novel design approach that combines numerical simulations with Machine Learning (ML) analysis to explore the key design parameters of ultra-low scale Junctionless Gate-All-Around (JL GAA) Field-Effect Transistors (FETs). Accurate 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the I-V characteristics of 10 nm JL GAA FET devices. The influence of variations in device geometry and doping concentration on analog Figures-of-Merit (FoMs), such as intrinsic gain (Av) and cut-off frequency, is thoroughly analyzed. The use of high-k dielectric materials is also explored for improving the frequency response in high-speed analog circuits. By leveraging ML techniques, the study identifies the optimal design parameters that enhance key analog performance metrics, enabling efficient prediction and optimization of device behavior. Our results highlight the importance of channel radius and channel doping in enhancing the intrinsic gain and frequency response in analog devices. Moreover, the investigated JL GAA FETs exhibit high intrinsic gain and frequency performances, making them ideal candidates for high-speed and high-gain analog circuits. The integration of machine learning techniques further streamlines the optimization process, leading to the identification of optimal parameters that maximize analog performance of the device.