bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Gradient-Accelerated Two-Stage Multiobjective Optimization Method for CO2-Alternating-Water Injection in an Oil Reservoir
Authors: Liu Shuaichen , Yuan Bin , Zhang Wei 
Published: January 2024

Summary
CO2-water-alternating-gas (CO2-WAG) is a reservoir development method that can simultaneously enhance oil recovery and achieve CO2 storage. However, improperly designed parameters for CO2 injection and oil production may significantly reduce the oil displacement efficiency and CO2 storage. Furthermore, optimizing the injection parameters is computationally expensive due to the high computational cost of the compositional simulation. This work aims to propose an efficient optimization method to obtain a series of well-control schemes that balance maximizing net present value (NPV) and CO2 storage for decision-makers. Given the number of CO2-WAG cycles and the duration of each cycle, we optimize the water injection rate, gas injection rate, and half-cycle for the injection well and the bottomhole pressure (BHP) for the production well. In this paper, a two-stage optimization strategy is proposed to enhance the optimization efficiency. The first stage performs the surrogate-assisted single-objective optimizations of each considered objective. It is designed to find the endpoints of the Pareto front that connect all solutions of the multiobjective optimization; this stage not only provides important search directions for the subsequent multiobjective optimization but also improves the accuracy of the surrogate model near the Pareto front. The second stage is the surrogate-assisted multiobjective optimization, which aims to find all the solutions along the Pareto front based on the Pareto endpoints obtained from the first stage. In addition, this study successfully combines the gradient of the objective functions with the meta-heuristic algorithm during the multiobjective optimization, which ensures a faster convergence to the global optimum. The proposed multiobjective optimization algorithm shows faster convergence than the conventional optimization methods when applied to the three multiobjective optimization test functions. Finally, a comparison with the conventional multiobjective optimization is conducted based on one test function and two benchmark reservoir simulation models to verify the correctness and efficiency of the proposed method. It is confirmed that the proposed method outperforms the conventional ones for the optimization of CO2-WAG injection.
Antibacterial mechanism of garviecin LG34 against S. aureus and L. monocytogenes and its application in milk preservation.
Authors: Gao Yurong 
Published: January 2024

Abstract

Purpose The objective of this study was to reveal the antibacterial mode of action of garviecin LG34 against S. aureus CICC 21600 and L. monocytogenes CICC 21633 and measure the inhibitions on these two foodborne pathogenic bacteria in milk.
Methods Antibacterial mechanism of garviecin LG34 was ascertained by its effect on the efflux of Potassium (K+) ions, extracellular electrical conductivity, UV-absorbing substances, potential across the membrane (ΔΨ) and cell permeability. The inhibitions of garviecin LG34 against S. aureus CICC 21600 and L. monocytogenes CICC 21600 in milk was studied by viable counting method.
Results Supplementation with 160 AU/ml of garviecin LG34 had a bactericidal effect on S. aureus CICC 21600 and L. monocytogenes CICC 21633. 160 AU/ml of garviecin LG34 resulted in the effusion of potassium ion and UV-absorbing substances, the leakage of cellular electrolytes and the dissipation of electrical potential across the membrane of these two food-borne bacteria. Moreover, the increase in cell permeability of both strains were observed by flow cytometer after cells treated with 160 AU/ml of garviecin LG34. Garviecin LG34 significantly inhibited the growth of these two food-borne bacteria in the dairy products especially in skimmed milk.
Conclusion Garviecin LG34 could cause pore formation, intracellular materials release and permeability increase of S. aureus CICC 21600 and L. monocytogenes CICC 21633, and could be applied to milk as bio-preservative.
Application of hydrological indices and GIS for the detection of areas at risk of erosion - case study Oued Rhumel watershed, East Algeria
Authors: Miloud Koussa , Berhail Sabri 
Published: January 2024

Abstract
In Algeria, the loss of water capacity in reservoirs is a major challenge in the face of a significant increase in water demand. This loss of resource is due to the sedimentation of reservoirs, one of the most serious consequences of erosion. The use of hydrological indices is widely recognised as a highly effective method of providing input data for the cartographic representation of erosion-prone areas within geographic information systems. In the present study, the hydrological indices essential for erosion modelling were obtained through meticulous processing and analysis of a digital elevation model. The various indices, including sediment transport index, composite topographic index and stream power index, were combined with other relevant issues such as slope, curvature, channel spacing and channel density,
The results of the survey have shown that a significant part of the Oued Rhumel catchment, located in the eastern region of Algeria, i.e. 24.35% or 1,292.72 km2, is located in an extremely high or high erosion zone, which requires the immediate implementation of conservation measures. In addition, a total of 190.81 km2 or 2,024.47 km2 is located in areas with a moderate or low risk of erosion. It is therefore evident that the use of primary and secondary derivatives of the digital elevation model, as well as morphometric parameters, through the application of a spatial analysis tool, is an effective and comprehensive method for identifying erosion-sensitive zones. This approach can be used on a regional or continental scale to facilitate watershed management and monitoring.
Multi-Representation Domain Attentive Contrastive Learning Based Unsupervised Automatic Modulation Recognition
Authors: Shi Xiaoran , Li Yu , Tan Haoyue , Yang Xinyao , Zhou Feng 
Published: January 2024

Abstract
Automatic modulation recognition (AMR) is a crucial technology in the domain of electronic reconnaissance. Benefiting from the powerful feature extraction ability of deep neural networks, the AMR algorithms based on supervised deep learning usually achieve pleasant performance. However, in noncooperative scenarios, high-quality and reliable modulation labels are difficult to obtain. Therefore, this paper proposes a novel unsupervised framework for AMR called Multi-representation Attentive domain Contrastive learning (MAC). Carefully utilizing unlabeled signal data with an unsupervised contrastive learning method across multi-representation domains is a more effective way than directly using different representations as inputs. MAC aims to obtain high-quality signal feature information from unlabelled signals. In detail, inter-domain and intra-domain contrastive learning maximize the mutual modulation features between different representation domains and preserve self-information in In-Phase and Quadrature (I-Q) representation, respectively. Furthermore, the Domain Attention (DA) module shifts the selection of representation domains from the signal level to the feature level. The performance of the proposed framework was assessed through
publicly available datasets. The conducted experiments clearly showed that MAC remarkably outperforms existing unsupervised algorithms in modulation recognition and exhibits superior generalization ability. Moreover, our framework can be extended to accommodate various representation domains and trained end-to-end, thereby significantly enhancing the efficacy of training deep neural networks for automatic modulation classification without the need for labeled data. These results highlight the substantial reduction in the disparity between unsupervised and supervised representation learning in the domain of AMR.
Independent component analysis applied to hermetic reciprocating compressors to predict noise sources
Authors: Neto Marlipe Garcia Fagundes , Duarte Marcus Antonio Viana 
Published: January 2024

Abstract

The quantification and hierarchization of noise sources in machines is essential for developing and building better projects. The independent component analysis methodology was applied to hermetic reciprocating compressors in this study to classify the main noise sources. Five methodologies are presented in the time and frequency domains, for which ERBM is better in the recovery of impulsive sources in the time domain and Jade stands out in the frequency domain for the recovery of harmonic sources. Compressor analysis is restricted to frequencies up to 1000 Hz due to signal synchronization and absence of shell vibrating modes. The effects of gas unbalance and pulsation are observed as the main sources, with the first being responsible for noise at frequencies close to 60 Hz, while pulsation is responsible for the 500 Hz bands.
A pipeline for analysis of allele specific expression reveals salinity-dependent regulation in Nile tilapia
Authors: Campo Aurora , Gershoni Moran , Doron-Faigenboim Adi , Kültz Dietmar , Cnaani Avner 
Published: January 2024

Abstract
Species living in changing environments require the acclimatization of individual organisms, which may be significantly influenced by allele specific expression (ASE). Data from RNA-seq experiments can be used to identify and quantify the expressed alleles. However, conventional allele matching to the reference genome creates a mapping bias towards the reference allele that prevents a reliable estimation of the allele counts. We developed a pipeline that allows identification and unbiased quantification of the alleles corresponding to an RNA-seq dataset, without any previous knowledge of the haplotype. To achieve the unbiased mapping, we generate two pseudogenomes by substituting the alternative alleles on the reference genome. The SNPs are further called against each pseudogenome, providing two SNP data-sets that are averaged for calculation of the allele depth to be merged in a final SNP calling file. The pipeline presented here can calculate ASE in non-model organisms and can be applied to previous RNA-seq data-sets for expanding studies in gene expression regulation.
Health and environmental risk assessment of mercury in outdoor and indoor dust in artisanal and small-scale gold mining area in Amansie-west district in the Ashanti Region of Ghana
Authors: Poku patrick Adu , Addai-Arhin Slyvester , Nimako Collins , Arrazy Syafran , Agustiani Tia , Jeong Huiho , Agusa Tetsuro , Ishibashi Yasuhiro , Kobayashi Jun 
Published: January 2024

Abstract
Mercury (Hg) pollution around artisanal and small-scale gold mining (ASGM) areas has been of much concern. Many studies have reported elevated Hg concentrations in environmental media and human hair, but studies on dust relating to inhalation exposure of Hg are limited. In this study, we investigated mercury (Hg) in indoor and outdoor dust to reveal environmental and human health risk around ASGM in Amansie West district, Ghana. Indoor and outdoor dust samples were collected from Manso Abore and Manso Nkwanta in Ashanti Region, Ghana. The Mean and median value of Hg concentration in the indoor dust were 2.2 ± 3.6 mg/kg (n = 31) and 0.72 mg/kg respectively while that of the outdoor dust were 0.19 ± 0.48 mg/kg (n = 60) and 0.042 mg/kg, respectively. Mean and median Hg concentration in indoor dust were about 11 and 17 times higher respectively than that in the outdoor dust. The Hg concentration in the indoor dust of the miner was statistically significantly higher than that of the non-miner in Manso Abore (p < 0.05) but was not significant in Manso Nkwanta. The geo-accumulation index of the outdoor dust ranged from unpolluted to extremely polluted while that of the indoor dust ranged from moderately polluted to extremely polluted. Health risk assessments suggested that there was no potential non-carcinogenic health effect for Hg exposure relating to the dust to residents living in rooms of miners and non-miners.
Evaluation of tumor uptake and bio-distribution of 99mTc-labeled 1-thio-β-D-glucose and 5-thio-D-glucose in vivo
Authors: Muehlberg Fabian , Mohnike Konrad , Grosser Oliver S , Pech Maciej , Goldschmidt Juergen , Smalla Karl-Heinz , Seidensticker Ricarda , Ümütlü Muzaffer Reha , Deniz Sinan , Ricke Jens , Steffen Ingo G , Öcal Osman , Seidensticker Max 
Published: January 2024

Abstract
Background
To investigate the capacity of 99mTc-labeled 1-thio-β-D-glucose (1-TG) and 5-thio-D-glucose (5-TG) to act as a marker for glucose consumption in tumor cells in vivo as well as to evaluate the biodistribution of 1-TG and 5-TG.
We investigated the biodistribution, including tumor uptake, of 1-TG and 5-TG at various time points after injection (0.5, 2 and 4 h) in human colorectal carcinoma and human lung adenocarcinoma (HCT-116, A549) xenograft bearing nude mice (N=4 per tracer and time point).
Results:
In vivo biodistribution studies revealed a maximum tumor-to-muscle ratio of 4.22 ± 2.7 and 2.2 ± 1.3 (HCT-116) and of 3.2 ± 1.1 and 4.1 ± 1.3 (A549) for 1-TG and 5-TG, respectively, with a peak at 4 hours for 1-TG and 5-TG.
Biodistribution revealed a high uptake in kidneys and liver for 1-TG and in the lung, liver, and kidneys for 5-TG.
Conclusions:
1-TG and 5-TG showed an insufficient tumor uptake for diagnostic use in human colorectal carcinoma and human lung adenocarcinoma xenograft model.
Bone controls browning of white adipose tissue and protects from diet-induced obesity through Schnurri-3-regulated SLIT2 secretion
Authors: Xu Ren , Li Zan , Shi Baohong , Li Na , Sun Jun , Zeng Xiangchen , Bok Seoyeon , Chen Xiaohui , Han Jie , Yallowitz Alisha , Debnath Shawon , Cung Michelle , Ling Zheng , Hong Yixiang , Li Gang , Koenen Mascha , Cohen Paul , Lu Hongbin , Greenblatt Matthew 
Published: January 2024

Abstract

The skeleton has been suggested to function as an endocrine organ controlling whole organism energy balance, however the mediators of this effect and the molecular links between osteoblast function and systemic energy metabolism remain unclear. Here, we utilized Schnurri-3−/− (Shn3−/−) mice with augmented osteoblast activity, to probe the general relationship between bone formation and metabolic syndrome. Shn3−/− mice display resistance against high-fat diet-induced obesity with improved glucose homeostasis and insulin sensitivity associated with enhanced browning of white adipose tissue (WAT). Conditional deletion of Shn3 in osteoblasts (Shn3Osx mice) but not adipocytes (Shn3Adipoq mice) recapitulates the obesity resistance phenotype seen in Shn3−/− mice, indicating that this phenotype is driven by the skeleton. Through in vitro co-culture and in vivo fat-pad transplantation assays, we demonstrate that cytokines secreted by activated osteoblasts lacking Shn3 expression promote WAT browning. Among them, we identify SLIT2 as a Shn3-regulated factor secreted by osteoblasts that regulates WAT browning, with osteoblasts serving as the major source of systemic circulating SLIT2. Lastly, AAV-mediated silencing of Shn3 phenocopied the lean phenotype and augmented glucose metabolism in Shn3-deficient mice. Altogether, our findings establish a novel bone-fat signaling axis via SHN3 regulated production of SLIT2 in osteoblasts, offering a therapeutic target with the potential to address both bone loss and metabolic syndrome.
Comparative Analysis of Pinus halepensis, Pinus brutia, and Pinus pinea Extracts: Chemical Composition and in vitro Bioactivities
Authors: CHAMMAM Amel , FILLAUDEAU Luc , ROMDHANE Mehrez , bouajila jaloul 
Published: January 2024

Abstract
Purpose Traditionally, medicinal plants were frequently used to treat various diseases. In this regard, Pinaceae species (various parts, residues, extracts) is one of the potential traditional plants with health issues such as antibacterial, anti-cancer, and antioxidant activities. In the context of biomass valorization (forest residues) and the development of a circular bioeconomy, pine species generate large amounts of unvalorized cones. In this study, different solvents were used to extract bioactive compounds and evaluate bioactivities from dried and ground pinecones from P. halepensis PA, P. brutia PB and P. pinea PP.
Methods Petal P and heart C from pinecones were manually separated and were milled to investigate successive solvent extraction with increasing polarity: Cyclohexane 1SV, ethyl acetate 2SV and methanol 3SV at 20 °C. Spectrophotometry was used to quantify the total phenolic content TPC and to evaluate the antioxidant and anticancer activities. Gas chromatography with mass spectrometry GC-MS and High-performance liquid chromatography with diode-array HPLC-DAD were used to identify bioactive compounds.
Results The P-3SV extracts showed the highest TPC values and had a significant antioxidant capacity. The extracts of 1SV and 2SV had moderate anticancer activity. HPLC analysis allowed the identification of 38 compounds, twenty-seven of which were not previously detected in these species. Forty-six volatile compounds were identified using GC-MS, thirty-three of which were detected for the first time in this species.
Conclusions This study highlights the considerable potential of pinecones as a valuable reservoir of bioactive compounds and suggests that they can contribute to advances in health.
Extracting Patient Lifestyle Characteristics from Dutch Clinical Text with BERT Models
Authors: Muizelaar Hielke , Haas Marcel , Dortmont Koert van , Putten Peter van der , Spruit Marco 
Published: January 2024

Abstract

Background: BERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted into classifying unstructured clinical notes on the basis of patient lifestyle indicators, especially in Dutch. This article aims to test the feasibility of deep BERT models on the task of patient lifestyle classification, as well as introducing an experimental framework that is easily reproducible in future research.
Methods: This study makes use of unstructured general patient text data from HagaZiekenhuis, a large hospital in The Netherlands. Over 148 000 notes were provided to us, which were each automatically labelled on the basis of the respective patients’ smoking, alcohol usage and drug usage statuses. In this paper we test feasibility of automatically assigning labels, and justify it using hand-labelled input. Ultimately, we compare macro F1-scores of string matching, SGD and several BERT models on the task of classifying smoking, alcohol and drug usage. We test Dutch BERT models and English models with translated input.
Results: We find that our further pre-trained MedRoBERTa.nl-HAGA model outperformed every other model on smoking (0.93) and drug usage (0.77). Interestingly, our ClinicalBERT model that was merely fine-tuned on translated text performed best on the alcohol task (0.80). In t-SNE visualisations, we show our MedRoBERTa.nl-HAGA model is the best model to differentiate between classes in the embedding space, explaining its superior classification performance.
Conclusions: We suggest MedRoBERTa.nl-HAGA to be used as a baseline in future research on Dutch free text patient lifestyle classification. We furthermore strongly suggest further exploring the application of translation to input text in non-English clinical BERT research, as we only translated a subset of the full set and yet achieved very promising results.
Contribution of genomic surveillance in the detection and monitoring of SARS variants- CoV-2 during the 6 pandemic waves in the Central African Republic from 2020 to 2023
Authors: RAFAÏ Clotaire Donatien , Lango-Yaya Ernest , Belizaire Marie Roseline Darnicka , Fa-Ti-Gbia Maurel Annicet Adonis Ouoko , Simaleko Marcel Mbeko , Roungou Jean- Baptiste , Senzongo Oscar , Koffi Boniface 
Published: January 2024

Abstract

Objective: The Covid-19 pandemic has highlighted the need to strengthen diagnosis and genomic surveillance capacities. The Central African Republic was able to manage 5 waves during health monitoring and integrated genomic surveillance as a surveillance tool in 2021.
The aim of this study is to report surveillance data from the National Laboratory of Clinical
Biology and Public Health and describe the landscape of circulation of SARS-CoV-2 variants.
Materials and methods: This is a retrospective, descriptive observational study over a period of 3 years (ranging from April 2020 to November 2023) carried out on a population of consenting volunteers, from throughout the CAR, tested by RT -PCR on nasopharyngeal samples with sufficient information in the LNBCSP databases. Sequencing is largely carried out at the INRB in KINSHASA and from May 2023 at the LNBCSP.
Results and discussion: Out of 97,864 RT-PCR tests carried out, 9,764 came back positive, which corresponds to a prevalence of 9.98%. The average age of the patients was 39.97 years ± 13.76 and the sex ratio M/F was 2.12. The positivity of RT-PCR tests was significantly associated with age (p=0.001), sex (p=0.013) and clinical manifestations. Ten variants circulated during the 5 waves recorded. The landscape of circulating variants was dominated by the Omicron (B.1.1.529), Delta (B.1.617.2) variants and especially by B.1.620 and B.640 which marked the second wave in CAR.
Conclusion: This retrospective series provides key information for understanding the history of the Covid-19 pandemic in the CAR. Risk factors are identified and the variant circulation landscape described. Strengthening national genomic surveillance capacities would help the country adopt a better response against this pandemic.
The Current Situation and Internal Pathway of Physical Literacy among Chinese Elementary School Students
Authors: li mengyu , Zan Ziqing , liu lizhu , you lili 
Published: January 2024

Abstract

Background: Physical literacy(PL) is pivotal in improving sedentary behaviors, enhancing intrinsic motivation for physical activity, and supporting the growth and development of adolescents. The study is to measure the current situation and internal pathway of physical literacy among Chinese elementary school students.
Methods: In this study, a questionnaire was developed to measure the PL of elementary school students in China. The results obtained from the questionnaire measurements were used to report on the levels of PL among elementary school students and to explain the internal causal relationships among the various dimensions of PL.
Results: The average score for physical literacy among primary school students is 63.46 points. The qualification rates for physical literacy in three academic stages are 76.35%, 73.45%, and 59.17%. As primary school students advance in grades, the qualification rate for physical competency increases. The qualification rates for physical motivation in the three academic stages are 91.03%, 61.30%, and 63.78%, while the qualification rates for physical participation are 14.48%, 9.97%, and 3.95%. The correlation between physical literacy and physical motivation consistently increases across the three grade levels (r=0.529, r=0.596, r=0.674, P<0.01), but correlations with physical competency (r=0.826, r=0.770, r=0.767) and physical participation (r=0.852, r=0.701, r=0.615) continuously decrease. Physical competency serves as the mediating variable in the influence of physical motivation on physical participation, explaining 56.40% of the effect.
Conclusion: As grades advance, the overall physical literacy qualification rate experiences a yearly decline. Physical competency explains 56.40% of the effect in the influence of physical motivation on physical participation.