bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Cow Dung in Modulating Soil Water Dynamics: A Comparative Analysis Different Soil Types
Authors: Jadav Gaurav , Dodia Sandhya , Kher Pradhumansinh , Tank Jignesh , Dhurav D K , Kataria Bharat , Markna J H , Bhatt V D 
Published: October 2024

Abstract

Water scarcity is a critical worldwide problem that is worsened by causes such as increasing population, climate change, and unsustainable agriculture methods. The depletion of water resources in several areas presents a substantial risk to the security of food, the advancement of the economy, and the sustainability of the environment. Tackle water shortage, it is necessary to implement inventive solutions in several sectors, particularly agriculture since it is responsible for most water use worldwide. This study investigates the impact of cow dung amendment on soil water dynamics across twelve different soil types. The Relative Water Content (RWC), Gravimetric Water Content (GWC), field capacity (FC), and porosity were tested for different soil types at different levels of cow dung addition expressed as a percentage of the total weight (D1(0%), D2(5%), D31(10%), D4(15%), and D5(30%)). Results show significant variations in soil water characteristics among different soil types and dung concentrations. Higher levels of cow dung led to increased RWC, GWC, and porosity with notable shifts in FC. The findings provide insights into the complex interactions between soil properties and organic amendments, offering valuable implications for sustainable soil management practices and agricultural productivity optimization.
The ASCEND technique - A modified direct aspiration first pass technique for a faster and cost-effective mechanical thrombectomy
Authors: lewis martin , siddiqui juveria , sciacca sara , singh vishwajeet , lynch jeremy , booth thomas , kandasamy naga , balasundaram parthiban 
Published: October 2024

Abstract

Purpose:
Direct aspiration first pass technique (ADAPT) has been the standard for aspiration thrombectomy (AT) in anterior circulation large vessel occlusion (AC-LVO) with modifications of the technique based on devices used and difficulties encountered. We introduce the ASCEND technique (Aspiration with Steam-shaped Catheter, Excluding additioNal Devices), and hypothesize that it improves catheter navigation, leading to time and cost savings in MT.
Methods:
This is a single institute, retrospective, pre-post intervention study, including consecutive patients with AC-LVO who underwent AT as first-pass technique. Patients were divided into two groups based on the first-pass technique attempted (ASCEND vs conventional ADAPT). Baseline characteristics, primary outcomes (first pass time, total procedure time, total additional devices and device cost) and secondary outcomes (recanalization, complications) were compared between groups. Multiple linear regression models were built for primary outcomes to look for the effect of steam-shaping when covariates are present to reflect real-world setting. Multi-national survey performed to introduce the technique and feedback obtained.
Results:
ASCEND (n=39) and cADAPT (n=40) groups were similar in baseline clinical characteristics. Anterior genu and ophthalmic segment were crossed in 94.9%, clot reached in 84.6%, and entire MT procedure completed in 59.0% of patients of the ASCEND group without use of additional materials. Groups were similar in performance and safety indicators. ASCEND technique was superior to conventional ADAPT with less first-pass time (8.9 vs 14.7 minutes), total procedure time (20.2 vs 35.4 minutes), additional devices used (0 vs 3) and cost involved (2083 vs 5830 £) per procedure (all P<0.05). Multiple linear regression models maintained improved primary outcomes with steam-shaping (all P<0.05). Neurointerventionalists who tried ASCEND (n=11) affirmed that it was safe and likely to save time and cost involved.
Conclusion:
ASCEND technique, involving a simple step of steam-shaping the aspiration catheter during MT can provide huge benefits in time and cost savings, without compromise of performance or safety.
WITHDRAWN: Compact Two Elements MIMO Antenna: Isolation and Gain Enhancement by Using FSS for Ultra-wideband Applications
Authors: Azharuddin Mohd , Mondal Kalyan , Singh Shougaijam Chinglemba 
Published: October 2024

Abstract

This work presents a two-element compact multiple-input multiple-output (MIMO) antenna for ultra wideband (UWB) applications. The antenna is fabricated on FR4 substrate with compact size of 20 × 28 ×1.6 mm3 (0.26λ0×0.37λ0×0.022λ0, where λ0 the free space wavelength at 4.2 GHz). Two tree-shaped radiators are used for implementing the MIMO antenna. The modified ground structure is used between two antennas to enhance the isolation. A pair of additional rectangular ground stubs are employed to further enhance the isolation and the impedance bandwidth as well. The gain and isolation of the MIMO antenna are further enhanced by incorporating a polarization insensitive angularly stable frequency selective surface (FSS) structure. The proposed model acquires an impedance bandwidth of 3.6 – 10.6 GHz with the accomplishment of isolation more than 20 dB and peak gain of 9.85 dBi. The MIMO ability of the antenna integrated with FSS is demonstrated in terms of standard envelop correlation coefficient (ECC), diversity gain (DG), TARC and time domain analysis. The measured ECC and DG are <0.005 and > 9.87dBi with the phase response executing the linear variation. All the characteristics values confirm that the proposed MIMO antenna is fit to be implemented as a good candidate for UWB applications.
Comparison between the effect of the preoperative localization by carbon nanoparticles and the patient's own blood of the Gastric cancer with uninvaded serous membranes
Authors: 邓 忠权 , Wu Jiaming , Zhu Yi 
Published: October 2024

Abstract

Objective
To compare the clinical value of endoscopically carbon nanoparticles localization and patients’ own blood localization in laparoscopic radical resection of non-invasive serosal gastric cancer.
Methods
A retrospective analysis was conducted on 90 patients with gastric cancer who underwent gastroscopic carbon nanoparticles or the patient's own blood localization followed by laparoscopic surgery at the Affiliated Hospital of Jiaxing University between January 2019 and January 2023.
Results
In laparoscopic surgery, the lesions' location was identified using local positioning markers on the stomach's serosal surface. Intraoperative exploration and evaluation of staining resolution, observation of the failure of injection on the operation and other conditions. All patients were located under gastroscopy, and postoperative pathology proved that the incision margin of specimens was negative.
Conclusions
Different positioning methods can be selected according to different requirements.
Cycles of snow algal blooms and snowmelt intensify Antarctic ice shelf melting
Authors: Liang Dong , Zhang Lu , Cheng Qing , Zhu Qi , Liu Yiming , Bashir Barjeece , Kong Weidong , Kong Lingyi 
Published: October 2024

Abstract

Antarctic ice shelves play a pivotal role in global sea-level regulation, yet their sensitivity to temperature variations, freeze-thaw cycles, and biotic factors such as snow algae remains under-explored. This study addresses the critical question: how do snow algae influence the melting dynamics of Antarctic ice shelves under changing climatic conditions? To answer this question, the study applies time-lag adjusted Pearson correlation and Granger causality tests to high-resolution Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine. The findings demonstrate that algae biomass significantly influences subsequent melting, underscoring snow algae’s pivotal role in accelerating the melting of Antarctic ice shelves. This research emphasizes the need to integrate biotic factors in models of polar ice dynamics and climate change projections. The study also provides a workflow for snow algae and snowmelt analysis at high resolution over large areas, contributing to a deeper understanding of snowmelt and global sea-level rise.
Mode switchable quantum well infrared detector with rectangular plasmonic microcavity structure
Authors: Zhao Jun , You Tongtong , Jiang Hengrui , Xiong Dayuan 
Published: October 2024

Abstract

In this paper, a quantum well infrared photodetector (QWIP) using a rectangular plasmonic microcavity structure is proposed. Based on the fact that different side lengths of the rectangular patch responding to different TM waves during surface plasmon resonance, the device has three operating modes. In the case of TM10 and TM01 waves being incident alone, the response peaks of the device are located at 6.48 µm and 7.38 µm, which correspond to the two single-peak modes of Ex and Ey polarisation, respectively. The two response wavelengths generated by the two polarisation modes are independently regulated within a certain range and are not affected by each other. In the case of simultaneous incidence of TM10 and TM01 waves, the full width at half maximum (FWHM) of the device's responsivity spectrum reaches 1.56 µm, which is broadband response mode. The simulation study reveals that the average electric field enhancement |EZ|/|E0| in all three modes is more than 5 times, while the single-peak and broadband response mode responsivities reach 3 A/W and 2 A/W, respectively. Our findings indicate that the average energy inside the microcavity is positively correlated with the microcavity volume within a certain range. Moreover, the coupling structure exhibits favourable incident angle compatibility, and the device performance in each mode is insensitive to the incident angle in the range of ~ 45°.
Modeling Hierarchical Functional Brain Networks via L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder for Multi-task fMRI Data
Authors: Cui Puwang , Liu Huan , Li Li 
Published: October 2024

Abstract

Modeling functional brain networks is essential for revealing the functional mechanisms of the human brain. Deep Neural Network (DNN) models have widely been employed for extracting multi-scale spatiotemporal features from functional Magnetic Resonance Imaging (fMRI) data. Nonetheless, existing DNN approaches often struggle with capturing generic temporal features across diverse tasks due to their reliance on fully connected layers. To overcome this limitation, this paper introduces a novel framework based on a L2 -Normalized Fully Convolutional Recurrent Attention Autoencoder (L2-FCRAAE), designed to model hierarchical functional brain networks (FBNs). The L2-FCRAAE framework is engineered with fully convolutional recurrent layers that notably exclude any fully connected layers. This design choice ensures adaptability to variable-length fMRI data, facilitating the acquisition of temporal dependencies within sequential data. Consequently, it effectively models temporal dynamics and helps in recognizing brain states from fMRI data. Moreover, the incorporation of normalized temporal and channel attention blocks into the encoder can prevent model overfitting during training, thereby enhancing the model's representation capacity. Experimental outcomes showcase that the proposed L2-FCRAAE exhibits superior capability and generalizability in capturing spatial and temporal patterns of FBNs. It robustly identifies task-related components and resting-state brain networks (RSNs) in a hierarchical manner. Overall, this study presents a novel approach for understanding the hierarchical organization of functional brain architecture. If this paper is accepted and the code is published.
Transcriptomic responses of arbuscular mycorrhizal symbioses with negative growth phenotypes
Authors: Diao Fengwei , Liu Ke , Wu Wenjing , Shi Xiangyuan , Wang Xiuhong 
Published: October 2024

Abstract

Arbuscular mycorrhizal (AM) fungi can play important roles in sustainable agriculture, given that they provide multiple benefits for numerous plant species. Conversely, negative plant growth effects induced by AM fungi are also commonly observed. At present, however, comparatively little information is available regarding the effects of AM fungi at the molecular level. In this study, compared with an absence of AM fungus inoculation, tomato seedlings grown in soil inoculated with Funneliformis mosseae or Rhizophagus intraradices were characterized by reduced shoot and root growth. To gain further insights into the underlying mechanisms at the molecular level, we performed transcriptomic analyses. We accordingly identified 190 and 870 differentially expressed genes (DEGs) in the F. mosseae vs. control and R. intraradices vs. control comparisons, respectively. KEGG enrichment analysis of the former 190 DEGs revealed significant enrichment of the “Protein processing in endoplasmic reticulum,” “Flavonoid biosynthesis,” “Flavone and flavonol biosynthesis,” and “Stilbenoid, diarylheptanoid, and gingerol biosynthesis” pathways, whereas “DNA replication,” “Photosynthesis - antenna proteins,” “Cutin, suberine, and wax biosynthesis,” “Protein processing in endoplasmic reticulum,” and “Glycerophospholipid metabolism” were identified as pathways significantly enriched with the latter 870 DEGs. GO functional analysis revealed that among both groups of DEGs, many genes were assigned the “Response to stimulus” term. Moreover, we established that many of the enriched terms were associated with stimulus and stress response processes, including response to salt stress, heat, and reactive oxygen species. Collectively, our findings in this study indicate that under the experimental conditions assessed, AM fungi may trigger defense-related responses in hosts, even though the symbioses were characterized by negative growth phenotypes. These findings will contribute to advancing our current understanding of AM fungi and highlight the fact that AM fungi should not be unthinkingly applied in agricultural production without due consideration of the prevailing conditions.
Engineered extracellular vesicles promote the repair of acute kidney injury by modulating regulatory T cells and the immune microenvironment
Authors: Xie Lulu , Zhang Kaiyue , Pan Kai , Su Xiaomin , Zhao Xiaotong , Rui Li , Wang Yixin , Pang Haotian , Fu Enze , Li Zongjin 
Published: October 2024

Abstract

Background
Acute kidney injury (AKI) is a common and severe clinical condition. However, the underlying mechanisms of AKI have not been fully elucidated, and effective treatment options remain limited. Studies have shown that immune cells play a critical role in AKI, with regulatory T cells (Tregs) being one of the most important immunosuppressive lymphocytes. Tregs proliferation can attenuate AKI, whereas depletion exacerbates kidney injury. Given that endothelial cells (ECs) are the initial cells that interact with immune cells when they invade the tissue parenchyma, ECs are closely associated with immune reactions.
Methods and Results
In this study, P-selectin binding peptide-extracellular vesicles (PBP-EVs) that target and repair ECs are engineered. Transcriptome sequencing reveals that PBP-EVs reduce the expression of inflammatory genes in AKI mice. Using high-resolution intravital two-photon microscopy (TPM), an increased recruitment of Tregs in the kidneys of AKI Foxp3-EGFP transgenic mice following PBP-EVs treatment is observed, as well as significant Lgr5+ renal stem cell proliferation in AKI Lgr5-CreERT2; R26mTmG mice. Additionally, PBP-EVs treatment result in reduced infiltration of inflammatory cells, pathological damage and fibrosis of AKI mice. Upon depletion of Tregs in Foxp3-DTR transgenic mice, we observe diminished therapeutic effect of PBP-EVs on AKI.
Conclusions
The experimental results indicate that PBP-EVs can promote the repair and regeneration of AKI by mitigating endothelial cell damage and subsequently modulating Tregs and the immune microenvironment. These findings provide novel insights and strategies for the treatment of AKI.
Transcriptome and metabolome changes uncover the potential mechanism of high-fat diet induced obesity under hypoxia condition
Authors: Fan Mingming , Chen Qingqing , Xu Zhiying , Fang Yimu , Cong Zhuangzhi 
Published: October 2024

Abstract

Aim: We aimed to investigated the key genes and metabolites in high-fat diet (HFD)-induced obesity model in hypoxia condition to uncover the potential mechanism.
Method: Mice model in control, HFD, CIH, and HFDCIH were established through diet and hypoxia treatment. Then liver tissue samples were collected for transcriptome and metabolome analysis. Following, differential analyses were conducted to selected the unique genes and metabolites in HFD-induced obesity in hypoxia condition. Finally, functional analysis and correlation analysis were used to investigated the pathways and potential mechanism in obesity under hypoxia.
Results: A total of 448 unique genes (like CD44, FCGR4, and COL6A1) and 36 unique metabolites were identified to be affected by obesity in hypoxia condition. The function of these key genes and metabolites indicated that these genes and metabolites may be related to metabolism-related function and pathways. The interaction between these genes and metabolites may be involved in Glutathione metabolism, Arachidonic acid metabolism, Histidine metabolism, Drug metabolism-cytochrome P450.
Conclusion: The study uncovered the key genes and metabolites in obesity under hypoxia in mice model, and indicated the potential mechanism, providing indicator for the following focus.
A Machine Learning Model for Algorithmic Optimization of Superannuation Schemes
Authors: MUKUNZI Winfred Katile , MUGANDA Brian Wesley , KASAMANI Bernard Shibwabo 
Published: October 2024

Abstract

This paper sought to address the challenge of designing a superannuation pension scheme by developing a machine learning-based recommendation model for optimal asset portfolio selection and allocation. Pension schemes face challenges in dealing with the uncertainties associated with financial markets, especially in selecting an appropriate assets portfolio that can optimize the Return-on-Investment. This study used various machine learning algorithms to build optimal portfolios, which were evaluated based on the portfolio’s return and Sharpe ratio. Data used was obtained from the annual financial reports on pension assets’ cost and market value as well as asset income for the period of July 2013 to June 2023. The Genetic Algorithm, Particle Swarm Optimization, K-means clustering, and Mean-Variance Optimization techniques were employed to construct optimal portfolios. Evaluation based on portfolio return and Sharpe ratio revealed the K-means cluster focused on government securities as a high-performing, low-risk option with 11.16% return and 7.11 Sharpe ratio. Conversely, the genetic algorithm and particle swarm optimized portfolio demonstrated a more diversified conservative asset allocation, leading to a mean return of 7.63% with a Sharpe ratio of 3.91, and a mean return of 6.61% with a 3.16 Sharpe ratio respectively. Comparing these constructed portfolios with OECD (2022) which reported that Kenya achieved a real investment return of 2.9% in 2021, signifies that 7/10 of the constructed optimal superannuation portfolios would have resulted in better performance.
The association between serum sodium variation during Pediatric Intensive Care Unit admission and the risk of clinical outcomes in critically ill children: a retrospective cohort study
Authors: Pournasiri Zahra , Nikparast Ali , Bakhtiary Mahsa , Hashemi Masumeh , Ahmadizadeh Seyyedeh Narjes , Behzad Azita , Asghari Golaleh 
Published: October 2024

Abstract

This study evaluates sodium variation during Pediatric Intensive Care Unit (PICU) admission and its association with prolonged PICU stay (≥7days) and PICU mortality. Data from 966 critically ill pediatrics (2019-2022) with at least two sodium values available during their PICU admission were analyzed. Patients were categorized based on all recorded sodium values into normonatremia, hyponatremia, hypernatremia, and mixed dysnatremia. The difference between the highest and lowest sodium values during PICU admission was used to identify sodium fluctuation. The statistical method used was Logistic regression model. During the PICU admission, 542 patients developed dysnatremia. The PICU-acquired dysnatremia was independently associated with higher prolonged PICU stay odds after controlling for major confounders. The PICU-acquired hypernatremia (OR:5.23;95%CI:1.72-15.92) and mixed dysnatremia (OR:2.89;95%CI:1.14-7.29) had significantly higher odds of PICU mortality. However, no significant association was found between PICU-acquired hyponatremia and odds of PICU mortality. Even mild sodium fluctuations (4-8mmoL/L) during PICU admission were significantly associated with higher prolonged PICU stay odds. The highest (≥15mmol/L) fluctuations in serum sodium levels during PICU admission were also significantly associated with higher odds of PICU mortality (OR:2.74;95%CI:1.11-6.79). Our findings underscore the importance of closely monitoring serum sodium during PICU admission to improve clinical outcomes.
AAGS: Appearance-Aware 3D Gaussian Splatting with Unconstrained Photo Collections
Authors: Zhang Wencong , Guo Zhiyang , Zhou Wengang , Li Houqiang 
Published: October 2024

Abstract

Reconstructing 3D scenes from unconstrained collections of in-the-wild photographs has consistently been a challenging problem. The main difficulty lies in different appearance conditions and transient occluders of uncontrolled image samples. With the advancement of Neural Radiance Fields (NeRF), previous works have developed some effective strategies to tackle this issue. However, limited by deep networks and volumetric rendering techniques, these methods generally require substantial time costs. Recently, the advent of 3D Gaussian Splatting (3DGS) has significantly accelerated the training and rendering speed of 3D reconstruction tasks. Nevertheless, vanilla 3DGS struggles to distinguish varying appearances of in-the-wild photo collections. To address the aforementioned problems, we propose Appearance-Aware 3D Gaussian Splatting (AAGS), a novel extension of 3DGS to unconstrained photo collections. Specifically, we employ an appearance extractor to capture global features for image samples, enabling the distinction of visual conditions, \eg, illumination and weather, across different observations. Furthermore, to mitigate the impact of transient occluders, we design a transient-removal module that adaptively learns a 2D visibility map to decompose the static target from complex real-world scenes. Extensive experiments are conducted to validate the effectiveness and superiority of our AAGS. Compared with previous works, our method not only achieves better reconstruction and rendering quality, but also significantly reduces both training and rendering overhead. Code will be released at https://github.com/Zhang-WenCong/AAGS.