bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Limonene production by probing the metabolic network through time-series metabolomics data
Authors: Khanijou Jasmeet Kaur , Scipion Clement P. M. , Borkar Shreyash , Chen Xixian , Chew Wee 
Published: April 2024

Abstract

Introduction
Limonene is a monoterpene with diverse applications in food, medicine, fuel, and material science. Recently, engineered microbes have been used to biosynthesize target biochemicals such as limonene.
Objective
Metabolic engineering has shown that factors such as feedback inhibition, enzyme activity or abundance may contribute to the loss of target biochemicals. Incorporating a hypothesis driven experimental approach can help to streamline the process of improving target yield.
Method
In this work, time-series intracellular metabolomics data from Escherichia coli cultures of a wild-type strain engineered to overproduce limonene (EcoCTs3) was collected, where we hypothesized having more carbon flux towards the engineered mevalonate (MEV) pathway would increase limonene yield. Based on the topology of the metabolic network, the pathways involved in mixed fermentation were possibly causing carbon flux loss from the MEV pathway. To prove this, knockout strains of lactate dehydrogenase(LDH) and aldehyde dehydrogenase-alcohol dehydrogenase (ALDH-ADH) were created.
Results
The knockout strains showed 18 to 20 folds more intracellular mevalonate accumulation over time compared to the EcoCTs3 strain, thus indicating greater carbon flux directed towards the MEV pathway thereby increasing limonene yield by 8 to 9 folds.
Conclusion
Ensuring high intracellular mevalonate concentration is therefore a good strategy to enhance limonene yield and other target compounds using the MEV pathway. Once high intracellular mevalonate concentration has been achieved, the limonene producing strain can then be further modified through other strategies such as enzyme and protein engineering to ensure better conversion of mevalonate to downstream metabolites to produce the target product limonene.
Rapid digestion of proteins with a dual-enzyme microreactor featuring 3-D pores formed by dopamine/polyethyleneimine/ acrylamide-coated KIT-6 molecular sieve
Authors: Yuan Fang-Fang , Lu Xin , Han Xiao-Jie , Qin Ting-Ting , Wang Pei , Bai Hai-jiao 
Published: April 2024

Abstract

The microreactor with two types of immobilized enzymes, exhibiting excellent orthogonal performance, represents an effective approach to counteract the reduced digestion efficiency resulting from the absence of a single enzyme cleavage site, thereby impacting protein identification. In this study, we developed a hydrophilic dual-enzyme microreactor characterized by rapid mass transfer and superior enzymatic activity. Initially, we selected KIT-6 molecular sieve as the carrier for the dual-IMER due to its three-dimensional network pore structure. Modification involved co-deposition of polyethyleneimine (PEI) and acrylamide (AM) as amine donors, along with dopamine to enhance material hydrophilicity. Remaining amino and double bond functional groups facilitated stepwise immobilization of trypsin and Glu-C. Digestion times for bovine serum albumin (BSA) and bovine hemoglobin (BHb) on the dual-IMER were significantly reduced compared to solution-based digestion (1 minute vs. 36 hours), resulting in improved sequence coverage (91.30% vs. 82.7% for BSA; 90.24% vs. 89.20% for BHb). Additionally, the dual-IMER demonstrated excellent durability, retaining 96.08% relative activity after 29 reuse cycles. Enhanced protein digestion efficiency can be attributed to several factors: (1) KIT-6's large specific surface area, enabling higher enzyme loading capacity; (2) Its three-dimensional network pore structure, facilitating faster mass transfer and substance diffusion; (3) Orthogonality of trypsin and Glu-C enzyme cleavage sites; (4) The spatial effect introduced by the chain structure of PEI and glutaraldehyde's spacing arm, reducing spatial hindrance and enhancing enzyme-substrate interactions; (5) Mild and stable enzyme immobilization. The KIT-6-based dual-IMER offers a promising technical tool for protein digestion, while the PDA/PEI/AM-KIT-6 platform holds potential for immobilizing other proteins or active substances.
Performance of standardized patient reported outcomes developed for spondyloarthritis in primary and concomitant forms of fibromyalgia
Authors: Tsiami Styliani , Dukatz Piet , Gkelaki Maria , Sewerin Philipp , Kiltz Uta , Baraliakos Xenofon 
Published: April 2024

Abstract

Background. In spondyloarthritides (SpA) and fibromyalgia (FM), patients suffer from generalized pain. The impact of FM on PRO validated in SpA has not been systematically studied.
Objective. Study the performance of PROs developed for SpA in patients with primary (p) FM without chronic inflammatory-rheumatic disease vs. SpA without and with concomitant (c) FM.
Methods. Patients with pFM, axSpA or PsA and indication for treatment adaptation were prospectively included. Standardized PROs were assessed: BASDAI, ASDAS-CRP, DAPSA, patient´s global assessment, BASFI, LEI, MASES, SPARCC Enthesitis Score and FIQ.
Results. 300 patients were included (100/diagnosis). More males were found in axSpA vs. PsA and pFM group (67, 33 and 2/100, respectively), while 12 axSpA (axSpA+) and 16 PsA (PsA+) patients had cFM. pFM patients showed significantly higher scores in all assessments vs. axSpA or PsA, with exception of ASDAS-CRP (3.3±0.6 in FM vs. 3.1±1.0 in axSpA) and duration of low lumbar morning stiffness. Similar results were also found in the subanalysis of female patients only. In addition, patients with axSpA+ or PsA+ showed no differences to patients with pFM, while significantly higher scores were found for FM, axSpA+ and PsA+ for almost all FIQ items compared to axSpA- or PsA-.
Conclusions. PROs originally developed for axSpA or PsA need to be interpreted differently in the presence or absence of cFM. ASDAS-CRP and duration of lumbar morning stiffness were not affected by cFM. FM-specific questionnaires also showed high scores in patients with SpA with cFM but not in those without.
A novel herbal aromatic essential oil for treating Chemotherapy-Induced Nausea and Vomiting: a mass spectrometry and network pharmacology study of the traditional Chinese medicine formula
Authors: Zhan Min , Sun Yuanfang , Yan Jun , Zhang Qiaoxuan , Han Liqiao , Zhang Pengwei , Lin Haibiao , Wan Zemin , Xiao Xue , Li Shasha , Huang Xianzhang 
Published: April 2024

Abstract

Objectives: This study aimed to investigate the substance basis and pharmacological mechanism of Dingxiang Shidi Decoction in preventing and treating Chemotherapy-Induced Nausea and Vomiting (CINV).
Methods: We employed advanced analytical techniques including UPLC-MS/MS and GC-MS to analyze the chemical components of Dingxiang Shidi Decoction. Network pharmacology and molecular docking techniques were applied to explore its pharmacological mechanisms.
Results: Our study established a comprehensive chemical composition database for Dingxiang Shidi Decoction, integrating UPLC-MS/MS qualitative components, GC-MS qualitative components, and network database entries, comprising 292 chemical compounds. Furthermore, we elucidated potential pharmacologically active components and pathways associated with alleviating CINV symptoms by Dingxiang Shidi Decoction. These pathways include inflammation reduction, modulation of neurotransmitter stimulation in the gastrointestinal tract, and regulation of cellular proliferation and differentiation. Notably, key active ingredients, predominantly aromatic compounds such as sabinene, (-)-α-cubebene, α-copaene, β-caryophyllene, aromandendrene, γ-muurolene, (-)-α-muurolene, α-phellandrene, α-terpinene, γ-terpinene, (+)-δ-cadinene, and Gomisin B, demonstrate significant binding affinity with multiple targets, notably AKT1, BCL2, EGFR, MTOR, and STAT3.
Conclusions: This study sheds light on the substance basis and pharmacological mechanisms of Dingxiang Shidi Decoction in CINV treatment, providing valuable insights for its clinical application and suggesting aromatherapy as a promising therapeutic approach. These findings provide insights into the multifaceted pharmacological mechanisms of Dingxiang Shidi Decoction and its potential applications in clinical settings for the treatment of chemotherapy-induced nausea and vomiting.
Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition
Authors: Meng Yinghui , Zhu Nuo , Li Duan , Nan Jiaofen , Yao Ni , Xia Yongquan , Han Chuang 
Published: April 2024

Abstract

Same limb motor imagery (MI) brain-computer interfaces can effectively overcome the cognitive disassociation problem of the traditional different-limb MI paradigm, and they can reduce the patient burden and extend the functionality of external devices more effectively. However, the electroencephalogram (EEG) MI features of same limb originate from one side of the brain, which poses a great challenge to MI EEG feature mining and selection as well as accurate decoding. To overcome this problem, we propose an adaptive feature selection strategy for subject-specific optimal frequency band based on regularized common spatial pattern (RCSP) and stepwise discriminant analysis, then combine the integrated classification strategy to accurately decode three types of single-limb MI tasks. As there are minor frequency band differences and huge variability for the same limb MI tasks, the optimal frequency band range for each subject was selected by stepwise discriminant analysis, and RCSP was used to extract spatial distribution features, which reduced the influence of the length of the time window and differences of the frequency bands. Then an integrated classification strategy based on multiple efficient classifiers is used for MI accurate recognition. The proposed method obtains 76.58% accuracy in the unilateral limb MI recognition task, which is 12.67%, 9.89%, 6.62%, and 7.90% higher than other traditional decoding methods such as CSP + LDA, FBCSP + LDA, FBCSP + C2CM, and FBCSP + SVM, respectively. Compared with Deep ConvNet and EEGNet, the decoding accuracy is improved by 16.93% and 7.33%, respectively. The experimental results show that our proposed highly efficient method improves the decoding accuracy for classifying different joints of unilateral limbs and has high promotion and application value.
Determination of Different Pollution Levels in Surface Waters for Irrigation Purposes in Two Large Basins with Different Land Uses by Using Multivariate Statistical Techniques
Authors: YURTSEVEN Ibrahim , PAMUKCU-ALBERS Pinar 
Published: April 2024

Abstract

This study aims to explain the spatial patterns of surface water pollution and investigate water quality for irrigation purposes in the Kızılırmak and Yeşilırmak basins in Turkey. Multivariate statistical techniques such as cluster analysis (CA), principal component analysis/factor analysis (PCA/FA), and multiple regression analysis (MRA) were employed to optimize statistical information and modeling of selected water quality parameters based on the Water Quality Index (WQI) and CORINE land use. The CA grouped the more than ten water quality observation stations within those basins into three groups (G1, G2, and G3) based on their quality properties and pollution levels. Backward stepwise mode discriminant analysis (DA) suggested that the two clusters better explained the spatial similarities. The PCA/FA applied to data sets of two special groups calculated three or four factors for both basins, capturing 81.07%, 72.16%, 73.83%, and 84.49% of the total variance, respectively. WQI values ranging from 34.61 to 63.87 showed the irrigation water quality of the sampling stations. Applying MRA to WQI and deriving the main parameters from PCA/FA demonstrated the efficiency of combining WQI and related irrigation water quality parameters analyzed by multivariate statistical techniques in this study. Based on the results of the WQI dissemination map, 48.59% of the Kızılırmak basin and 98.98% of the Yeşilırmak basin fall within the “high restriction” category. In the Yeşilırmak basin, 2.21% of the total basin area is categorized as “severe restriction”. The remaining areas of both basins need “moderate restrictions”. Maps of the WQI dissemination on CORINE land use classes illustrate that the surface water of basins requires some extent of treatment before consumption. The results of this study have been used to identify major problems in terms of irrigation water quality in both basins and contribute to planning processes for decision-makers.
Agriculture Enhancement Using Machine Learning With React
Authors: Lakshmanan V , Ramachandiran R , Ragul B , Pradeep Sanjay 
Published: April 2024

Abstract

Crop recommendation is a crucial duty in agriculture for optimizing crop yield and soil control practices. Traditional techniques rely closely on professional information and manual analysis, which can be time-eating and at risk of human errors. In recent years, machine mastering strategies have emerged as effective gear for automating and improving those obligations. This research paper gives a novel technique to crop and soil advice the usage of gadget getting to know algorithms. The take a look at makes use of a dataset containing various soil attributes together with corresponding crop kinds. Two device learning fashions, logistic regression and random wooded area, are trained at the dataset to expect suitable crops based totally on soil situations. Additionally, an ensemble version using gradient boosting machines (XGBoost) is explored to further improve prediction accuracy. Experimental results display that the proposed device getting to know fashions achieve high accuracy in crop advice, with the XGBoost model outperforming the logistic regression and random forest algorithms. The trained models provide the insights into the relationships among soil attributes and crop suitability, aiding farmers in making informed selections for crop selection. Overall, these studies contribute to the advancement of precision agriculture by way of leveraging gadget gaining knowledge of techniques to decorate crop recommendation systems. The findings have implications for sustainable agriculture practices, aid optimization, and increased crop productivity.
Effectiveness of green cupric oxide nanoparticles for walnut storage pest management
Authors: Rodríguez Maria Rosa , Comelli Nieves Carolina , López Tamara Elizabeth , Matías Mariana del Huerto Sánchez , Denett Gabriel Omar , Bracamonte Daniela Milagros , Pietro Eduardo Daniel , Diez Patricia , González-Baró Ana Cecilia , Sampietro Diego Alejandro 
Published: April 2024

Abstract

Walnut yield and quality are often affected by beetle infestations, particularly those caused by Carpophilus dimidiatus (Fabricius) (Nitidulidae) and Oryzaephilus mercator (L.) (Silvanidae). Beetle damage exposes walnut to microbial food spoilers such as Fusarium species. Insecticides currently used for beetle control are environmentally unfriendly. Pest-suppressing nanomaterials can generate innovative nanopesticide technologies leading to more sustainable and efficient farming practices. This work explores a green synthesis approach for copper oxide nanoparticles (CuO-NPs) in basic medium by hydrolates obtained from Lippia integrifolia and Pimpinella anisum, denoted as CuO-I and CuO-A, respectively. Characterization through XRD, FT-IR, Raman, UV-Visible absorbance, and AFM techniques, indicated that CuO-A and CuO-I have a size ranging 2-10 nm height. Antifungal assay showed that both have a similar efficacy (MID = 320 µg), 3-fold stronger than CuO- NPs obtained in absence of hydrolates (denotes CuO-W) (MID = 960 µg), with the broadest inhibitory halos (ID = 126-128 mm) observed for CuO-A. Insecticidal activity of CuO-NPs showed a concentration-dependent behavior, with CuO-I showing an effect comparable to diatomaceous earth. SEM images confirm the adhesion of nanoparticles to insect surfaces, which could induce oxygen deprivation and disrupting of metabolic processes. Both CuO-A and CuO-I are promising for their use in integrated pest control in walnut storage.
Effect of gas pressure on the creep and seepage characteristics of low-permeability coal
Authors: Li Xiangchun , Zeng Jianhua , Shi Yaoyu , Zhang Shuhao , Li Xiaowei , Long Yuzheng , Liu Rong , Zhang Liang 
Published: April 2024

Abstract

Coal, as a complex gas-bearing medium, exhibits unique rheological behavior under mining disturbance. However, with increasing mining depth, the creep and seepage mechanisms of low permeability coal remain unclear. Therefore, to investigate the coupled relationship between creep and gas seepage in low permeability coal, time-dependent triaxial experiments were conducted in this study. To consider the effects of gas, a modified creep model based on the Nishihara creep model was developed and validated by numerical simulations and experimental results. The correlation between coal creep and seepage was then analyzed under different gas pressure conditions, showing a significant reduction in Young's modulus during creep. This reduction indicates a strong correlation between creep and gas seepage, which is supported by the agreement between creep strain and permeability curves. In addition, the results show a significant attenuation effect in the seepage process at different gas pressures due to pore pressure and adsorption. It is worth noting that unlike conventional soft coal, no permeability reduction was observed in the initial stage due to the low permeability and stiffness of the coal. And it was shown that the presence of methane accelerated the creep behavior of the coal, resulting in a decrease in permeability. Overall, this study provides important insights into the rheological behavior of low-permeability coal under mining disturbance and sheds light on the mechanisms governing gas seepage in coal.
Automated Audio Dataset Generation Tool for Classification Tasks in Marine Science
Authors: Gnyś Piotr , Szczęsna Gabriela , Domínguez-Brito Antonio C. , Cabrera-Gámez Jorge 
Published: April 2024

Abstract

This study presents a comprehensive data processing pipeline designed to convert diverse labeled audio recordings into a uniform labeled dataset suitable for input into machine learning algorithms. The primary emphasis lies in applying this pipeline to marine audio classification, with an illustrative example from this domain serving as experimental validation for the proposed approach.
Performing 3 Phase Short Circuit Analysis on Single Line Diagram of a Power System using ETAP
Authors: MEHTRE VISHAL VAMAN , Jha Ankit 
Published: April 2024

Abstract

One essential instrument for guaranteeing the secure and dependable operation of power systems is short circuit analysis (SCA). This study looks into the use of ETAP software for SCA on a power system's single line diagram (SLD). The goals of the study are to Emphasize the role that SCA plays in power system protection strategies. Use SLDs to illustrate how the power system is represented. Describe the SCA functionalities of the ETAP software. A summary of the methodology section will be given, detailing how to create and run a SCA within ETAP with the supplied SLD. The main conclusions will be discussed, including the following: • Determination of fault currents at systemically important places. • Determining whether possible equipment overloads occur in fault circumstances.
Classification of FESEM Images of Nanostructures by Noval Deep Learning Algorithm
Authors: Patil Ranispoorti R , N Jayalaxmi G , Choudapur V. H. , Baligar Vishwanth P. 
Published: April 2024

Abstract

SEM (Scanning Electron Microscopy) takes nanoscale pictures, whereas DL (Deep Learning) analyses data using neural networks. Image interpretation is streamlined by the collaboration of SEM and DL, which automates SEM image analysis and material characterisation. This integration improves productivity by quickly extracting relevant information from enormous datasets, highlighting subtle patterns that would otherwise be difficult to detect manually. Authors of this article consider 3 classes of SEM images of various magnifications and a total of 93 images is being produced for analysis and processing. Authors intend to enhance, categorize, and segment the pictures collected. SEM pictures are classified based on doping substance with the host material pure Cobalt Chromite, Neodymium doped Cobalt Chromite and Lanthanum doped Co-balt Chromite. Image quality and appearance are improved via augmentation techniques. The similarity and complexity of images in all three classes and the inclusion of images of different magnification posed a challenge in classification which hinder the accuracy rate of classification process. So authors use the statistical results of the tests to create semi-automatic method for classifying and labeling pictures generated by the SEM. Authors propose a low complexity algorithm, aimed to extract features and increase model performance. This approach offers efficiency gains by minimizing time and computational resources compared to pre-trained models, while maintaining consistent classification results. Achieved a training and testing accuracy of 100% and 78.26% respectively. SEM pictures are also classified using CNN and pre-trained models VGG16, Inception v3. To evaluate and compare performance, comparative studies are used to measure model correctness. SEM images are segmented into regions of interest using an integrated technique that combined the watershed and contours. A segmented picture is used to calculate the surface area of a Cobalt Chromite sample. The surface area of material is determined to be 41,02,628 nm 2 .
Gelation of Hydrophilic Polymer Bearing Metal-coordination Units with Au(III) Ions: Application to Synthesis of Porous Gold
Authors: Nagai Daisuke , Mano Aoi , Ishii Takafumi , Okamoto Shusuke 
Published: April 2024

Abstract

We describe an environmentally benign method for the synthesis of porous gold by gelation of a hydrophilic polymer bearing metal-coordination units (thiocarbonyl groups, denoted as HPMC) and Au(III) ions. The gelation was performed by dropwise addition of a dispersed aqueous solution of HPMC to an aqueous solution of Au(III) in a test tube. Concentrations of 15 and 20 wt% HPMC provided elongated and fibrous gels. Scanning electron microscopy and transmission electron microscopy analyses of the fibrous gels revealed the formation of porous gels containing Au nanoparticles. Calcination of the polymer parts in the porous gels at 550 oC for 7 h, followed by self-assembly of the remaining Au nanoparticles, provided the golds with micrometer-size pores. Thermogravimetric analysis of the porous golds indicated that its purity was high (96∼99%). Because the metal-coordination unit has soft basic characteristics, it preferentially coordinate to soft acidic noble metal ions such as platinum group metal ions, Ag(I), and so on. Therefore, this method will be applied to synthesis of various porous metals.