bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between sleep duration and depression in adults with hypertension: a cross-sectional study
Authors: Chen Lehui , Lv Tian , Wang Xiaoling , Zhao Han , Zhu Qiongbin , Li Jie
Published: July 2024

Background
There is an increasing awareness of sleep duration and depression in hypertension. This study provided a theoretical basis for early screening of depression in patients with hypertension by analyzing the relationship between sleep time and depression in patients with hypertension.
Methods
The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) from 2004 to 2015. Depressive symptoms were tested using the Patient Health Questionnaire (PHQ-9), and depression was diagnosed when PHQ-9 total score >10. The association between sleep duration and depression in hypertension was estimated using weighted multivariate logistic regression, and its nonlinearity was examined by restricted cubic spline (RCS) regression. Multivariate logistic regression analysis was used to identify independent risk factors for depression in hypertensive patients, a risk prediction nomogram model was established, and the receiver operating characteristic (ROC) curve was drawn to evaluate its ability to discriminate depression in hypertensive patients.
Results
A total of 8,696 hypertension participants were included in this study. Multivariate logistic regression showed that an increment of sleep duration was strongly associated with depression in hypertension (OR = 0.772, 95%CI = (0.705, 0.883). The RCS curve showed L-shaped relationships between sleep duration and depressive risk in hypertension. The nomogram model showed a favorable discriminatory power with an area under the curve (AUC) of 73.4%. Sleep duration at 7.22 hour might be most beneficial for the mental health of patients with hypertension.
Conclusion
Our study demonstrated the relationship between sleep duration and depression in patients with hypertension, Effective interventions for sleep disorders in patient with hypertension are needed for preventing depression.
Nomogram Model Establishment for Short-Term Survival Prediction in Hypothyroidism Patients with Heart Failure Based on the MIMIC-IV Database
Authors: Lou Wenfeng , Guo Yikun , Hu Tielin , Yan Zewen , Qi Shuo , Ding Zhiguo
Published: July 2024

The objective of this study was to develop an efficient nomogram model for predicting short-term survival in hypothyroidism patients with heart failure. Methods: Retrospective data was extracted from the Medical Information Mart for Intensive Care (MIMIC-IV) open-source clinical database. Patients diagnosed with hypothyroidism and heart failure upon admission were randomly assigned to the training and testing sets. Independent prognostic factors were determined through univariate and multivariate Cox regression analysis. The correlation between variables was assessed using the variance inflation factor. A nomogram model was constructed based on the identified prognostic factors, and risk scores were generated. The model's performance was assessed using the C-index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Result: A total of 656 hypothyroidism patients with heart failure were included in the study. Age, Sequential Organ Failure Assessment (SOFA) score, blood glucose (Glu), blood urea nitrogen (BUN), creatinine (Cr), respiratory rate (RR), and Charlson Comorbidity Index (CCI) score were identified as independent risk factors for patient mortality and were incorporated into the final model. The C-index for the training and testing sets was 0.763 and 0.721, respectively, indicating good accuracy of the model. Evaluation of the ROC curve, calibration curve, and decision curve further confirmed the model's robust and reliable predictive performance. Conclusion: In conclusion, our study successfully developed a convenient and effective nomogram model for predicting short-term mortality in hypothyroidism patients with heart failure. We identified several independent risk factors, including age, respiratory rate, serum creatinine, blood glucose levels, etc. These findings have implications for developing individualized therapeutic plans, enhancing patient management and follow-up, and ultimately improving disease outcomes.
Expert consensus on the structure and content of an enhanced care pathway for psychological changes after stroke in the UK: A modified Delphi survey
Authors: Hobden Georgina , Tang Eugene Yee Hing , Demeyere Nele
Published: July 2024

Background
Enhancing long-term support for post-stroke cognitive impairment is a top research priority. Addressing current gaps in UK post-stroke cognitive care provision requires a pragmatic and scalable intervention that can be integrated in the existing stroke care pathway. This study aimed to develop consensus on core features of a UK-based monitoring and psychoeducational intervention for cognitive changes after stroke.
Methods
An expert panel of UK healthcare professionals and researchers participated in an online modified Delphi survey. Candidate intervention features were identified from clinical guidelines, existing literature, research team/collaborator expertise, and PPI group lived experience. Survey participants indicated whether they agreed/disagreed/had no opinion about including each candidate feature in the intervention and free-text responses were invited. We analysed responses for consensus (≥ 75% agreement) using descriptive statistics, with items not reaching consensus carried into subsequent rounds. Template analysis was used to identify similarities/differences in viewpoints for items that did not reach consensus.
Results
The survey rounds were completed by 36, 29 and 26 participants, respectively. Participants agreed reviews should include a stroke-specific cognitive screen (97% agree) and assessment of other psychological changes (low mood, anxiety, fatigue: 94%, 90%, 89% agree, respectively). They agreed stroke survivors should be offered at least one review, regardless of their psychological presentation in hospital. They agreed on the importance of various psychoeducation topics, and formal (100% agree) and informal (79% agree) training for those conducting reviews. Consensus was not reached on the review mode (in person/remote options: 67% agree), offering reviews one-year post-discharge to patients without impairments detected in hospital (68% disagree), or including a dementia screen (63% disagree) and/or neuropsychological assessment battery (58% disagree). However, there were similarities in participant viewpoints – for example, participants highlighted the importance of onwards referral where clinically indicated.
Conclusions
The UK-based post-stroke monitoring and psychoeducation intervention was originally conceptualised as a cognitive care pathway, but expert participants agreed on the importance of also addressing related psychological changes (eg low mood, anxiety). There was clear consensus on a minimum set of intervention features. Recommendations outlined here may usefully inform local service improvements. Future research will evaluate the impact of the intervention on patient outcomes.
MtL-NFW: A Meta-Learning Framework for Automated Noise Filter Selection and Hyperparameter Optimization in Auto-ML
Authors: Khan Irfan , Zhang Xianchao , Kumar Ramesh , Alhashmi Saadat M. , Ali Rahman
Published: July 2024

The extensive implementation of machine learning (ML) has transformed data analysis and decision-making processes. However, the process of choosing suitable ML algorithms for a specific task remains challenging, commonly referred to as the Algorithm Selection Problem (ASP) in academic literature. Automated machine learning (Auto-ML) tackles algorithm selection and hyperparameter adjustments by automating these processes, hence decreasing the required time and expertise. The current study is focused on the automated selection of noise filters for the task of classifying data with noisy labels. Class label noise, resulting from erroneous data labels, has a greater effect on the learning process compared to attribute-level noise. The existing Auto-ML approaches for selecting suitable noise filters overlook hyperparameter adjustment, which is important for the effectiveness of noise filters. Instead, it relies on computationally demanding techniques such as grid search. In addition, the current Auto-ML algorithms utilize random noise imputation techniques in their methodologies that are not 1 able to adequately capture the complex noise distributions typically present in real-world datasets. In order to overcome these limitations, we propose the introduction of Meta-learning for Noise-Filter Workflow Selection (MtL-NFW), an innovative framework that automate and integrate adequate hyperparameter selection along with noise filter for a certain task. This eliminates the need to rely on manual and computationally intensive methods for hyperparameters settings. Our second contribution is to leverage and employ the Neighbor-wise noise imputation technique to our framework, that induce noise based on proximity to class boundaries, generating more robust meta-training data. Our framework evaluates 114 classification datasets with six noise filtering algorithms and Random Forest as the base classifier, extracting six groups of meta-features and generating a meta-knowledge base of 51,642 configurations. XGBoost trained as meta-learner, predicts and recommends optimal noise filter and hyperparameter combinations for new datasets. Empirical validation on 114 OpenML datasets shows that MtL-NFW significantly improves the accuracy and robustness of noise filter recommendations, outperforming four state-of-the-art Auto-ML methods.
The effect of telomeres in cervical cancer
Authors: Xu Cong , Xu Yonghong , Cao Qing , Luo Guoling , Yu Jingwen , Wang Guangming
Published: July 2024

Globally, cervical cancer ranks as a prevalent cancer among women and stands as the fourth leading cause of mortality in gynecological cancers. Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model. Within the discovered TCGA group (p < 0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients.
Gum Arabic Magnetized Graphene Oxide Composite for the Removal of Fluorescent Dye Fluorescein from Aqueous Solutions: Equilibrium, Kinetic and Thermodynamic Studies
Authors: Ali Anwar , Khitab Fatima , Shah Jasmin , Jan Muhammad Rasul
Published: July 2024

In recent years, due to industrialization water pollution is the major issue. The aim of the present work was the synthesis of solid Gum Arabic Magnetized Graphene Oxide (GA-MGO) and its application for the removal of fluorescent dye, fluorescein from aqueous samples. The characterization of the composite was carried out through FTIR, SEM, EDX, point of zero charge, and surface area. Various parameters optimization such as pH, shaking time, adsorbent dose, adsorbate concentration, temperature, and their influence on the removal of fluorescein using GA-MGO composite during the adsorption process was studied. The synthesized GA-MGO exhibited high adsorption efficiency of 94.18 % in 60 mins (pH 2, fluorescein 20 µg mL-1, 0.08 g L-1 of adsorbent). The synthesized GA-MGO composite has a high capacity to remove fluorescein molecules from aqueous solution at 25 °C. The kinetics of adsorption was investigated and it was found that adsorption followed pseudo-second-order kinetics with the regression coefficient R2 value of 0.962. The R2 value of Langmuir (0.985) and Freundlich (0.894) showed that the Langmuir adsorption isotherm fitted best to the adsorption process. Different thermodynamic parameters, Gibb’s free energy and Van’t Hoff equation reveals tht the proposed reaction is spontaneous and exothermic. Regeneration of adsorbent/ desorption of dye and application of composite to the sample of sewage and industrial effluents results in better adsorption. The finding reveals the feasibility of GA-MGO composite as potential precursor for the effective adsorption of fluorescent dyes due to its high surface area.
Changes in various forms of fibronectin in patients undergoing coronary artery bypass grafting with cardiopulmonary bypass
Authors: Lemańska-Perek Anna , Krzyżanowska-Gołąb Dorota , Wysoczański Grzegorz , Barteczko-Grajek Barbara , Goździk Waldemar , Adamik Barbara
Published: July 2024

Coronary heart disease is one of the most common lifestyle diseases and one of the treatment methods is coronary artery bypass grafting. The use of cardiopulmonary bypass (CPB) during this procedure is associated with the transient activation of a systemic inflammatory response and may lead to several intra-and postoperative complications. Fibronectin (FN), an adhesive glycoprotein that is a key component of the extracellular matrix, is widely distributed in human body fluids. It is one of the endogenous mediators involved in both the inflammatory response and coagulation. We aimed to detect changes in the level of fibronectin and its possible fragmentation or forming of complexes with fibrine during coronary artery bypass grafting with cardiopulmonary bypass. Our results indicate that the CPB procedure is associated with significant changes in levels of fibronectin. Elevated levels after surgery, as well as slight increases in the amount of FN degradation products in relation to the duration of CPB may suggest that these increases are related to extracellular matrix remodeling resulting from cardiac surgery and the initiation of repair processes. The results indicate that fibronectin has clinical potential as a marker of repair processes.
YOLOX-SEH: Efficient Small Target Detection in Remote Sensing Images
Authors: Song Qingzeng , Zhang Honggeng , xue yongjiang
Published: July 2024

Object detection is a crucial and challenging problem in computer vision. In remote sensing image object detection, objects can appear in any orientation, complicating accurate detection. Additionally, the presence of small objects and environmental factors pose significant challenges to existing deep learning-based object detection algorithms. To address these issues, this paper proposes a YOLOX-based object detection algorithm named YOLOX-SEH. To enhance the detection accuracy of objects appearing in any orientation, a Swin Transformer structure is incorporated into the YOLO framework along with other optimizations. Specifically, the Swin Transformer employs sliding windows and hierarchical structures for more efficient and flexible computation, capturing global information from multiple scales with improved feature extraction capability. Additionally, an Explicit Visual Center (EVC) structure is introduced to capture global long-range dependencies through a lightweight MLP network, enhancing the accuracy and efficiency of object detection algorithms. To improve the detection of small objects, an additional prediction head is added to detect tiny objects, complementing YOLOX’s original three prediction heads for effective multi-scale object detection. Extensive experiments on the DOTA v1.0 and DIOR datasets demonstrate that our YOLOX-SEH performs well in remote sensing image detection with a small model size of only 8MB.
An image analysis method to study the relationship between seagrasses and their epiphytes
Authors: Huang Chi , Mehrubeoglu Mehrube , Piñón Carissa , Cammarata Kirk
Published: July 2024

Seagrasses are globally threatened due to increasing environmental stressors in coastal ecosystems. Excessive accumulation of algal epiphytes is suggested to be harmful to seagrasses, but an understanding of the quantitative relationship between epiphyte accumulation and effects on seagrass is still incomplete. The biomass, pigment and morphological measures widely used in understanding seagrass-epiphyte relationships provide limited spatiotemporal resolution of the dynamics of epiphyte colonization relative to host leaf growth and senescence. In this study, color scanning and image analysis methods were developed and validated to characterize epiphyte accumulation relative to seagrass host morphology. Thalassia testudinum collected monthly or bimonthly from July 2019 to April 2020 in Redfish Bay, Texas, was analyzed through traditional and image-based measures. Spectral Angle Mapper (SAM) algorithms within the ENVI Program distinguished the pixels of many epiphytes collectively from epiphyte-free leaf areas based on RGB color bands. Misclassified pixels were < 10% in 94% of the analyzed images. Classification accuracy was also evaluated by correlation of traditional biomass and morphology metrics vs. image-based metrics for more than 2,000 seagrass and epiphyte images collected across different seasons and environmental conditions. Image-derived seagrass metrics strongly correlated with traditional metrics, but linear regressions of epiphyte biomass vs. epiphyte area (pixels) and biomass ratio of epiphyte to seagrass vs. epiphyte coverage (epiphyte pixels/leaf pixels) were weaker. This was likely due to seasonal and environmental influences on both the epiphyte community composition and the seagrass-epiphyte relationship, since image analysis does not capture the depth of epiphyte growth on the leaf. The image analysis method provides insight into the spatiotemporal accumulation of epiphytes on the seagrass host and has the potential to be adapted to detect environmentally-induced changes in the community composition of epibiota.
The role of emotional stability in employees' job competence, job stress, and mental health: a moderated mediation model
Authors: Liang Zhengyan , Zhang Mingqiang , Tang Tian , Ye Hong , Yang Mei , Yao Min , Chen Jiayu , Li Hao , Zeng Yuqing
Published: July 2024

This study focused on the role of emotional stability in employees' job competence, job stress, and mental health. The research utilized surveys conducted online and on-site, with 839 valid questionnaires collected by police officers in China. The findings revealed that job stressors had a full mediating effect on the relationship between job competence and mental health. Emotional stability not only moderates competence and stressors but also moderates stressors and mental health. However, the results of the multigroup structural equation model revealed that job competence was positively correlated with job stressors in civilian police officers but not in auxiliary police officers. Our study aids in predicting for whom will most likely be related to increased or decreased job stressors and mental health (i.e., individual low emotional stability); these findings have important theoretical and managerial implications.
A predictive model for identifying pulmonary lesions based on CT features of rimmed signs and satellite lesions: A multicentre study
Authors: cao zhenyu , Xu Gang , Xu Jianying , Tian Fengjuan , Shi Hengfeng , Yang Dengfa , Xie Zongyu , Wang Jian
Published: July 2024

Objectives
To verify a differential prediction model based on different lung pathology types utilizing the rimmed sign (RS) and satellite lesion (SL) methods to enhance identification efficiency.
Methods
From January 2015 to October 2023, the clinical data and chest CT images of 3030 patients with lung lesions were analysed. The lesions were divided into three groups: malignant, inflammatory, and benign. The pairwise identification models were constructed separately with (Models1-3) or without (Models1*-3*) rimmed signs and satellite lesions, and the developing and test group were divided by 7:3. Six models were built to distinguish between these groups (Model1 or Model1*: malignant vs. inflammatory; Model2 or Model2*: malignant vs. benign; Model3 or Model3*: inflammatory vs. benign). The curve (AUC) was calculated to evaluate the performance of these models. The Delong test was used to compare the differences between different models.
Results
In the test group, the AUC and Accuracy of Models1-3 and Models1-3* were 0.920/84.8%, 0.990/96.2%, 0.881/76.4%, and 0.900/73.5%, 0.989/90.1%, and 0.869/78.6%, respectively. The Delong test showed no significant difference between Models1-3 and Models1-3* (p > 0.05), Accuracy (T1, T2) > Accuracy (T1*, T2*), Accuracy (T3) < Accuracy (T3*).
Conclusions
The six prediction models in this study effectively differentiated among different types of lung lesions, with the rimmed sign and satellite lesion features improving the accuracy of Model1 and Model2.
Construction and Application of Early Stratification Dynamic Prediction Model for Bronchopulmonary Dysplasia in Extremely Premature / Very Low Birth Weight Infants
Authors: An Ning , Yang Jingwen , Zhang Rong , Han Wen , Zhou Xuchen , Yang Rong , Zhu Yanping , Zhao Ting , Li Mingxia
Published: July 2024

Objective
To investigate the independent risk factors for Bronchopulmonary Dysplasia (BPD) at different time points within the first week in extremely premature/very low birth weight infants and to construct an early stratification dynamic prediction model for BPD through machine learning, aiming to achieve dynamic prediction of BPD for the early identification of high-risk groups and preemptive prevention.
Methods
A retrospective collection of clinical data was conducted on premature infants admitted to the Neonatology Department of the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, with gestational age (GA) < 32 weeks or birth weight (BW) < 1500g. Eligible subjects were randomly divided into training and validation sets in a 7:3 ratio for model building and internal validation. Prospective clinical data from preterm infants admitted to six neonatal rescue centers in various districts of Xinjiang from January to October 2023 were independently collected to validate the practical application value of each model. Clinical parameters were collected, and study participants were divided into three groups: no BPD, mild BPD, and moderate to severe BPD (msBPD). Machine learning predictive models for BPD stratification employing logistic regression (LR), random forest (RF), XGBoost (XGB), and gradient boosting decision tree (GBDT) were constructed for postnatal days 1, 3, and 7. Comprehensive evaluation was performed to select the optimal model at each time point and proceed to external validation.
Results
The study retrospectively gathered data from 554 preterm infants (286 no BPD, 212 mild, and 56 msBPD cases). Prospectively, 387 preterm infants (208 no BPD, 138 mild, and 41 msBPD cases). On ordinal logistic regression, GA, BW, prenatal steroids, interruption of umbilical blood flow, severe preeclampsia, FIO2, CRP, RBC, systemic inflammatory response index (SIRI), prognostic nutritional index, platelet mass index, alveolar-arterial oxygen difference, and oxygenation index were independent risk factors for BPD severity at different times after birth. After comprehensive evaluation, the LR and XGB models were identified as better BPD stratification prediction models for postnatal days 1, 3, and 7 (AUC = 0.810,0.837 and 0.813 respectively).
Conclusion
Early stratification dynamic prediction machine learning models for BPD have been constructed for postnatal days 1, 3, and 7 in extremely premature/very low birth weight infants. These may serve as effective tools for the screening of high-risk BPD populations.
Quantifying the Non-isomorphism of Global Urban Road Networks using GNNs and Graph Kernels
Authors: Tian Linfang , Rao Weixiong , Zhao Kai , Vo Huy T.
Published: July 2024

The concept of quantifying graph non-isomorphism aims to establish a more flexible framework, overcoming the stringent limitations inherent in graph isomorphism. This approach enables the measurement of the degree of difference between two graphs using appropriate metrics, making it broadly applicable in scenarios where strict graph isomorphism is not required. In this paper, we employ Graph Neural Networks (GNNs) and graph kernels to quantify the non-isomorphism of road networks, analyzing 10,361 road networks from 30 cities worldwide. Our results indicate that Edge Convolutional Neural Network (EdgeCNN) outperforms the Weisfeiler-Lehman (WL) kernel.Therefore, we challenge the assertion that "GNNs are at most as powerful as the WL test in distinguishing graph structures," highlighting that this claim overlooks the diversity and significance of node attributes and is limited to homogeneous graphs. In practice, node attributes are crucial in graph evaluation. Compared to graph kernels, GNNs can more effectively and comprehensively leverage node attributes, thereby enhancing the accuracy of heterogeneous graph classification. By quantifying graph non-isomorphism, we not only gain deeper insights into the differences in urban road networks but also provide quantitative evidence for urban road network planning, promoting more scientifically sound urban development strategies.
Histograms of projected normal vector distribution for 3D local surface description
Authors: Sa Jiming , Zhang Xuecheng , Zhang Chi , Song Yuyan , Ding Liwei , Huang Yechen
Published: July 2024

Existing methods for 3D local feature description often struggle to achieve a good balance in distinctiveness, robustness, and computational efficiency. To address this challenge, a novel 3D local feature descriptor named Histograms of Projected Normal Vector Distribution (HPNVD) is proposed. The HPNVD descriptor consists of two main components. First, a Local Reference Frame (LRF) is constructed based on the covariance matrix and neighborhood projection to achieve invariance to rigid transformations. Then, the local surface normals are projected onto three coordinate planes within the LRF, which allows for effective encoding of the local shape information. The projection plane is further divided into multiple regions, and a histogram is computed for each plane to generate the final HPNVD descriptor. Experimental results demonstrate that the proposed HPNVD descriptor achieves a good balance among distinctiveness, robustness, and computational efficiency. Moreover, the HPNVD-based point cloud registration algorithm shows excellent performance, further validating the effectiveness of the descriptor. The source code for the proposed method and the dataset used in the paper can be accessed at: https://github.com/zhangxuecheng1998/HPNVD-descriptor-and-datasets. The DOI for this repository is 10.5281/zenodo.12567370.
An Efficient and Robust Distributed Vertex Coloring Algorithm in Wireless Networks
Authors: Miri Mohammadhasan , Ahmadian Ali , Hadei Sayed A.
Published: July 2024

The distributed vertex coloring is one of the most intensively studied problem in graph theory with many applications in computer networks.We propose a deterministic distributed algorithm for this problem in wireless networks.The algorithm's run-time is divided into rounds.In each round, every uncolored vertex or at least one of its neighbors obtains a color equal to the round number and informs its neighbors of the taken color with a 1-bit message. Therefore, the bit complexity is minimum. Moreover, the set of colored vertices is a maximal independent set (MIS), and the maximum number of colors or rounds equals the maximum number of MISs that cover the graph's vertices. It is bounded by the maximum vertex degree and also the radius of the graph.This is the first algorithm that uses a lower bound on the number of colors for every graph.In the case of trees, the logarithm of the number of vertices is another upper bound for it. Finally, we propose another algorithm for 2-coloring, and the number of rounds is bounded by the diameter of the tree.
Vertical structure of the cross-equatorial northerly surge observed during the Year of Maritime Continent–Cold Surge Observation in 2021
Authors: Moteki Qoosaku
Published: July 2024

This study investigated the vertical structure of 6 cross-equatorial northerly surge (CENS) events during the Year of Maritime Continent–Cold Surge Observation in 2021 (YMC-CSO2021) campaign. These events, named CENS1 (Jan. 18﹣20), CENS2 (Jan. 29﹣30), CENS3 (Feb. 2﹣5), CENS4 (Feb. 5﹣9), CENS5 (Feb. 18﹣20), and CENS6 (Feb. 25﹣26), occurred under different environmental conditions associated with cold surges (CSs) and the Madden–Julian Oscillation (MJO). Using radiosonde observations, we identified distinct characteristics in the northerly wind layer thickness, westerly wind bursts, and potential temperature anomalies among the events. Notably, CENS6 featured a deep northerly wind layer reaching 400 hPa, influenced by a southward pressure gradient linked to a cyclone in the Southern Hemisphere. Statistical analysis of past CENS events revealed higher occurrences during La Niña years and active MJO phases over the western Pacific, consistent with the YMC-CSO2021 observations. Composite analysis showed that CENS events induced significant ascending motion and localized potential temperature gradients, leading to positive precipitation anomalies around the Maritime Continent. These findings enhance our understanding of CENS dynamics and their impact on regional climate variability.
Spectral Computed Tomography in the Assessment of Lymph Nodes in Cervical Cancer Patients Treated With Definitive Radiotherapy: a Single-Center, Prospective Study
Authors: Zeng Zheng , Chen Yining , Sun Yuliang , Zhou Bing , Xu Haoran , He Lei , Hu Ke , Qiu Jie , Zhang Fuquan , Yan Junfang
Published: July 2024

Purpose
Identifying metastatic lymph nodes (LNs) in patients with cervical cancer treated with definitive radiotherapy may inform treatment strategy and determine prognosis, but available methods have limitations. Herein, we aimed to evaluate the performance of quantitative parameters in spectral computed tomography (CT) scanning in this context.
Materials and Methods
Patients with cervical cancer, who underwent pretreatment spectral computed CT simulation scanning and planned radiotherapy, were enrolled in this prospective study. The LNs were categorized as “metastatic” and “non-metastatic”, based on 18F-fuorodeoxyglucose positron emission tomography computed tomography (18F-PET/CT), magnetic resonance imaging, and follow-up results. Iodine concentrations (IC), normalized IC (NIC), effective atom number (effZ), and spectral curve slope (λHU) in the arterial (AP) and venous (VP) phases, were compared. Univariate and multivariate logistic regression analyses were used to determine spectral CT factors independently associated with LNs metastasis, and their diagnostic efficacies were assessed using the area under the curve (AUC) analysis.
Results
A total of 115 metastatic and 97 non-metastatic LNs were detected. IC, NIC, effZ, and λHU values in the AP and VP differed between the LN groups. The AP parameters had superior diagnostic performance compared to the VP parameters (AUC from 0.902 to 0.909). In univariate and multivariable logistic regression analysis, λHU in the AP and NIC in the VP were independent predictors for metastatic LNs and their combination improved AUC to 0.923, with a sensitivity of 84.4%, and a specificity of 85.6%.
Conclusions
Quantitative spectral CT parameters can help distinguish metastatic from non-metastatic LNs in patients with cervical cancer treated with definitive radiotherapy. Combination of λHU in AP and NIC in VP further improves diagnostic performance.
Effective Bioremediation of Heavy Metals for Environmental Sustainability Approach using Bacillus subtilis HIB2 and Pseudomonas aeruginosa HIB11 isolated from Hindon River, India
Authors: Kumar Deepak , Kumari Sangeeta
Published: July 2024

The goal of this work was to isolate, identify, and characterize heavy metals degrading bacterial strains from the contaminated Hindon river (Yamuna's tributary) in India. Hindon river water quality was also assessed using physico-chemical and heavy metals analyses, revealing the river's degraded state. The bacterial isolates were isolated from Hindon river. Two bacterial isolates coded as HIB2 and HIB7 were screened and selected for further study based on the resistance to heavy metals (Cd, Ni, and Pb). Finally, these were identified based on morphological, biochemical tests and 16S rRNA sequencing. Biodegradation potential for heavy metals was assessed using inductively coupled plasma mass spectrometry (ICP-MS). The bacterial isolates HIB2 and HIB11 from Hindon river were identified as Bacillus subtilis and Pseudomonas aeruginosa respectively. At 10 mg/L initial concentration, results of biodegradation experiments showed the removal competences of 53.9% for Cd2+ and 68.6% for Ni2+ and 71.7% for Pb2+ using Bacillus subtilis HIB2, where 57.6% for Cd2+ and 66.4% for Ni2+ and 70.7% for Pb2+ using Pseudomonas aeruginosa HIB11 after 14 days of incubation at pH 7.0 and 37°C. Both isolates revealed the similarity in removal efficiency for these three heavy metals. The ability of Bacillus subtilis HIB2 and Pseudomonas aeruginosa HIB11 degrading heavy metals was found to be through curing experiments. The study showed that the Bacillus subtilis HIB2 and Pseudomonas aeruginosa HIB11 could be involved in effective degradation of heavy metals (Cd, Ni, and Pb).
Assessing Porewater and Sediment Quality in the Sidi Salem Dam: Insights from an Artificial Aquatic Geosystem in Tunisia
Authors: Helali Mohamed Amine , Ouameni Imen , Mna Hayfa Ben , Mesnage Valérie , Sassi-Souissi Radhia , Kouka Ahmed , Oueslati Walid
Published: July 2024

The concentration profiles and diffusion fluxes of PO43−, NH4+ and NO3− at the water-sediment interface, along with the chemical speciation of phosphorus and heavy metals (Pb, Zn, Cu, Cd, and Cr), were analyzed in three sediment cores from the Sidi Salem Dam lake, the largest dam in Tunisia. The results show that phosphorus in the sediment is associated with carbonates (P-Ca 40 to 60%) and iron from 0 to 10 cm depth (P-Fe 25 to 30%). This association seems to be related to pH regarding P-Ca and Eh regarding P-Fe. The PO43− and NH4+ fluxes are either zero or directed towards the water column (7 to 16 µmol m2 d− 1) for NH4+, excepting NO3−, whose flux at the dam entrance is directed towards the sediment (-8.44 µmol m−² d− 1). Cd contents are negligible in all cores; Zn and Cr contents are highest in the three studied sites; and Pb and Cu are mainly high at the dam entrance (20 to 43.7 µg g− 1 at 10 cm depth for Pb). Chemical fractionation shows that Pb, Zn, Cu, and Cr are mainly associated with the residual phase (41%, 71%, 53%, and 95%, respectively).
Autogenous Electron Acceleration by Ion Flow Vortex in Space Plasmas
Authors: Liu Chengming , Cao J. B. , Liu Yangyang , kronberg Elena , Xing Xining , Zhao B. N , Daly Patrick
Published: July 2024

Plasma flow vortexes are ubiquitous in space and astrophysical settings, and play a vital role in energy and mass transport, contributing to formation of stars and planets, launching and dissipation of astrophysical jets, and coupling between planetary magnetospheres and ionospheres. Here we present the first in situ observation showing that energetic electrons with energies up to 250 keV can be directly generated inside an ion flow vortex with finite spatial scale in space. The electron acceleration is achieved by establishment of Fermi acceleration trap and parallel electrostatic potential within the flow vortex, leading to dramatic enhancement of energetic electron flux by at least an order of magnitude. The accelerated electrons can drive mass and energy transport along local magnetic field lines, and serve as free energy source for broadband electrostatic emissions. The proposed acceleration mechanism driven by flow vortex can be fundamentally important for understanding energetic phenomena in the cosmos, such as nonthermal emissions near astrophysical jets and killer electrons in geospace.
Nanoscale Visualization of the Chromatin Remodeling as Potent Biomarker in Aggressive Breast Cancers
Authors: Nandi Chayan , Anjum Farhan , Kaushik Kush , Salam Abdul
Published: July 2024

Chromatin organization, predominantly histone modifications is crucial to regulating gene expression. Genomic instability caused by the aberrant chromatin structure leads to various cancer subtypes. However, genome-wide higher-order structures of euchromatin and heterochromatin and understanding their dynamical ratio concerning cancer are poorly characterized. Here, utilizing super-resolution microscopy, we studied the remodelling structural aspects of euchromatin and heterochromatin in various cancer subtypes including the most aggressive triple-negative breast cancer (TNBC) lacking targeted therapies and is difficult to diagnose. The nanoscopic visualization revealed a prominent reorganization of euchromatin and heterochromatin ratios and a distinct spatial distribution of chromatin domains among breast cancer subtypes with a special emphasis on the TNBC and HER-2 positive. Increased expression of remodelling protein SSRP-1 supports the structural changes of chromatin in all breast cancer cells. These observations, contemplate the possibility of euchromatin and heterochromatin domain ratios in predicting and understanding the breast cancer subtypes and can be targeted for personalized cancer therapy.
On the automatic recognition of Jeongganbo music notation: dataset and approach
Authors: Kim Dongmin , Han Danbinaerin , Jeong Dasaem , Valero-Mas Jose J.
Published: July 2024

The Jeongganbo notation, the first music representation system in East Asia capable of jointly expressing pitch and duration, has been extensively used -and still is- in the Korean music tradition since its inception in the 15th century. In this regard, there exists a plethora of music works that exclusively endure as physical sheets, which not only constitutes a heritage preservation challenge due to the inherent degradation of this format but also impedes the use of computational tools to study and exploit this music tradition. While the Optical Music Recognition (OMR) field, which represents the research area devoted to devising methods capable of automatically transcribing music sheets into digital formats, has addressed this issue in a number of music notations from the Western tradition, no previous research has considered the preservation of Jeonganbo scores. In this context, this work presents the following contributions: (i) the first data assortment of real Jeongganbo scores for OMR tasks; (ii) a collection of synthetic data generation and augmentation mechanisms to alleviate the scarcity of manual annotation; and (iii) a neural-based transcription scheme based on state-of-the-art OMR strategies specifically tailored to Jeongganbo scores. The experiments performed prove the validity of the approach - performance rates close to a 90% of success - and open new research avenues for under-resourced yet challenging music notations.
Unified Description of Charge Density Waves in Electron- and Hole-doped Cuprate Superconductors
Authors: Choi Jaewon , Tu Sijia , Nag Abhishek , Tam Charles , Tippireddy Sahil , Agrestini S. , Lin Zefeng , Garcia-Fernandez Mirian , Jin Kui , Zhou Ke-Jin
Published: July 2024

High-temperature cuprates superconductors are characterised by the complex interplay between superconductivity (SC) and charge density wave (CDW) in the context of intertwined competing orders. In contrast to abundant studies for hole-doped cuprates, the exact nature of CDW and its relationship to SC was much less explored in electron-doped counterparts. Here, we performed resonant inelastic x-ray scattering (RIXS) experiments to investigate the relationship between CDW and SC in electron-doped La2-xCexCuO4. The short-range CDW order with a correlation length ~35 Å was found in a wide range of temperature and doping concentration. Near the optimal doping, the CDW order is weakened inside the SC phase, implying an intimate relationship between the two orders. This interplay has been commonly reported in hole-doped La-based cuprates near the optimal doping. We reconciled the diverging behaviour of CDW across the superconducting phase in various cuprate materials by introducing the CDW correlation length as a key parameter. Our study paves the way for establishing a unified picture to describe the phenomenology of CDW and its relationship with SC in the cuprate family.
Tissue-resident macrophages favor early lung cancer development via direct physical interactions with cancer cells
Authors: Benaroch Philippe , Nikolic Jovan , Joseph Ackermann , Taheraly Sarah , Mabire Morgane , Gobert François-Xavier , De Folmont Apolline , Thibaut Ronan , Morin Mathieu , Ait-Mohamed Ouardia , Gerena Edison , Goyard Sophie , Mesple Jeremy , Salmon Helene , Rose Thierry , Amar Martine Ben , Joanny Jean Francois
Published: July 2024

While extensive research has unraveled diverse biochemical mechanisms underlying the pro-tumorigenic activities of macrophages, the role of physical forces in early tumor development remains elusive. Here, we integrated experimental data with physical modeling to investigate the contribution of various physical forces. Monitoring the growth of KrasG12Dp53-/- (KP) lung tumor spheroids in vitro in 3 dimensions using real-time microscopy accurately reflects the role of tissue-resident macrophages, rather than monocytes, in promoting tumor growth. Based on the quantitative data, we develop a physical model of 3D spheroid growth to simulate the dynamics of proliferating cancer cells and interacting macrophages, aligning closely with experimental results. When cultured alone, KP lung cancer cells form a single aggregate that contracts over time due to limited nutrient access at the center. The introduction of macrophages promotes the nucleation of cancer cells into multiple small aggregates, which grow before fusing to form a large final aggregate. Throughout this process, macrophages facilitate nutrient access and thus favoring cell division. The theory predicts that adhesion forces between tumor cells and macrophages are essential for the observed tumorigenic effect. Among integrins potentially involved, CD11c is expressed by alveolar macrophages but not by monocytes. Blocking CD11c reduces adhesion forces, preventing spheroid nucleation by macrophages, and impairing growth. Additionally, our theory predicts the redistribution of macrophages towards the periphery of tumor aggregates over time, an evolutionary pattern that we validated in mice in vivo. These findings underscore the significant contribution of physical interactions via CD11c between tissue-resident macrophages and cancer cells in the pro-tumor effect of macrophages. Targeting this interaction could offer a new therapeutic strategy to impede early tumor development and metastasis.
Nature-Inspired Low Carbon Building Materials With Lightweight, Strong and Tough Properties by Tailoring Hierarchically Porous Structure
Authors: Lu Zeyu , Jiang Jinyang , Wang Han , Lin Junlin , Wang Fengjuan , Liu Zhiyong , Wang Liguo , Li Yali , Li Zongjin , Li Yunjian
Published: July 2024

Conventional building materials have been faced with significant challenges, including large carbon emissions, high density, and quasi-brittleness. Inspired from the hierarchical porous structure in nature, a low-carbon, lightweight, strong and tough cement-based material (LLST) was developed through in situ self-assembly strategy, which was accomplished by a rapid gelation of hydrogel as skeleton and subsequent deposition of cement hydrates as skin in order. As a results, the LLST exhibited hierarchical structure made up of sponge-like micropores (1 ~ 50 µm) and nanopores (5 ~ 100 nm), without detrimental macropores that compromise lightweight, strength, and toughness coordination. Compared with the normal cement paste, LLST displayed a 54% reduction in density, 145% and 1460% improvement in specific compressive strength and fracture energy, with only 36% carbon emission, which has not been realized in literature. Furthermore, such significant advancements were in depth revealed by ab initio metadynamics simulations, indicating that strong interactions, including van der Waals forces, hydrogen bonding and steric hindrance effects at atomic level, were generated between functional groups in hydrogels and Ca ion released from cement hydration. These findings not only bring a novel strategy for developing lightweight building materials with low-carbon emission and remarkable mechanical properties, but also provide valuable insights to realize the coordination of lightweight, strength and toughness by tailoring the hierarchical pore structure.
Granulosa cells provide transcriptomic information on ovarian follicle dynamics in the southern white rhinoceros
Authors: Klohonatz Kristin , Durrant Barbara , Sirard Marc-André , Ruggeri Elena
Published: July 2024

Much remains unknown about the reproductive physiology of southern white rhinoceros (SWR) and the effect of ovarian stimulation prior to ovum pickup (OPU) have not been fully elucidated. Granulosa cells (GC) provide valuable insight into follicle growth and oocyte maturation status. The goals of this study were to evaluate transcriptomic changes in GC from three stages of follicle development and to identify biomarkers possibly associated with follicular growth and maturation because of ovarian stimulation. GC collected from SWRs following OPU were assigned stages based upon follicle size. Total RNA was isolated, and cDNA libraries were prepared and sequenced on a NovaSeq 6000. All bioinformatics analyses were performed utilizing the Galaxy web platform. Reads were aligned to CerSimCot1.0, and the manual curation was performed with EquCab3.0. Overall, 39,455 transcripts (21,612 genes) were identified across follicle stages, and manual curation yielded a 61% increase in gene identification from the original annotation. Granulosa cells from preovulatory follicles expressed the highest number of unique transcripts. The following seven biomarkers were determined based upon cluster analysis and patterns of expression:COL1A1,JMY, FBXW11,NRG1, TMPO,MACIRandCOL4A1. These data can be used to potentially evaluate the effects of different ovarian stimulation protocols on follicle dynamics, improve OPU results, and support conservation efforts in this species.