bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-dose copper and blue light increases the yield of value-added biomolecules in Kirchneriella contorta (Chlorophyceae)
Authors: Aoyagi Leonardo Murilo , Baracho Douglas Henrique , Lombardi Ana Teresa 
Published: May 2023

Abstract
Microalgae are a natural source of valuable compounds with a wide range of applications. Given their physiological plasticity, strategies aiming at increasing the content of biomolecules have been proposed. In this study, low-dose copper and blue/red lights were combined and used as a manipulative strategy to induce biomolecule accumulation in Kirchneriella contorta (Schmidle). Cultures were exposed to a 1.6x10− 9 to 1.7x10− 8 mol L− 1 free copper (Cu2+) concentration range under white light for 48 h. Afterwards, the white light was replaced with blue or red lights for up to 96 h. We evaluated population growth through growth rates, photosynthesis through pulse amplitude modulated fluorescence (PAM) and biomolecules by quantifying chlorophyll a, carotenoids, carbohydrates and proteins. The results showed that at 3.8x10− 9 mol L− 1 Cu2+ and blue light, 2x more carotenoids, 4x more carbohydrates and 3x extra proteins were accumulated in the cells compared to the control. Among the light colors, the blue light most significantly interfered with the alga metabolism compared to the other colors. Although growth rates and quantum yields were not affected, non-photochemical quenching (NPQ) increased under blue light, showing that its higher energy compared to red and white lights may have impacted the photosynthetic apparatus. This study contributes to the biotechnology of microalgae by proposing a manipulative strategy that triggered the build-up of biomolecules in K. contorta.
Affective temperaments show stronger impact on infertility treatment success compared to somatic factors, highlighting the role of personality focused interventions
Authors: Szabo Georgina , F Judit Szigeti , Sipos Miklos , Varbiro Szabolcs , Gonda Xenia 
Published: May 2023

Abstract
Infertility has a multifactorial background, where, besides somatic factors, psychological contributors also play a role in development and outcome. While affective temperaments have been associated with development, course, and outcome as well as treatment success in various somatic conditions, their association with infertility and its treatment has not been investigated so far. The purpose of our retrospective cohort study was to evaluate the influence of affective temperaments on fertility treatment outcomes. Among 578 women who underwent infertility treatment in an Assisted Reproduction Centre in Budapest, Hungary, treatment success, detailed medical history and demographic parameters were recorded, and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Auto-questionnaire (TEMPS-A) was administered. Possible predictors of assisted reproduction outcome were analysed using multi-variate logistic regression models, followed by a receiver operating curve (ROC) analysis in order to define ideal affective temperament cut-off values for clinical applicability. Aside from age, BMI, and previous miscarriage, cyclothymic scores >4 (OR = 0.51 CI: 0.35–0.74, p < 0.001), depressive scores >9 (OR = 0.59 CI: 0.4–0.87, p = 0.009) and anxious scores >9 (OR = 0.45 CI: 0.31–0.66, p < 0.001) significantly decreased the odds of clinical pregnancy by 49%, 41% and 55%, respectively.Irritable and hyperthymic temperaments, as well as other somatic and socio-economic factors had no effect on infertility treatment outcomes. The results suggest that affective temperaments robustly influence the outcome of the infertility treatment. Thus, screening for affective temperaments may help identify high risk patient groups and offer patient tailored treatment, which may increase the chances of a successful pregnancy and live birth for women undergoing IVF treatment.
Bi-stage energy scheduling of residential energy hub with smart demand response programs
Authors: Darabi Mohammad 
Published: May 2023

Abstract
The scheduling of the energy generation is one of the main problems in the energy companies. The investigates to feed the demand are developing with new approaches such as demand side participation and onsite generation via storage systems. This paper proposed the optimal coordination framework of an energy scheduling in the storage-based residential energy hub (EH) system. This framework is performed as two-stage multi-criteria optimization approach using demand side participation. In the first stage, minimizing demand side’s bill is formulated using electrical demand load curtailment (ELDC) strategy, whereby energy demand is decreased. The optimized energy demand in first stage optimization is implemented in second stage to solve multi-criteria problem such as minimizing the generation costs and maximizing the consumers’ satisfaction level. In addition to ELDC participation in first stage, onsite generation is provided via storage systems include electrical storage system (ESS), heat energy storage (HES) and hydrogen storage system (HSS) in second stage by demand side. Also, impact of the ELDC strategy on the storage systems lifetime in order to reducing energy consumption in the charge state of the storage systems is assessment. The proposed optimization approach is solved using shuffled frog leaping algorithm (SFLA). Since, in second stage optimization multi-criteria problem is considered. The desired optimal solution in Pareto frontier solutions is found by LINMAP and fuzzy procedures. Finally, numerical simulation is implemented based on two case studies to demonstrate effectiveness of the proposed approach.
ECMO PAL: Using Deep Neural Networks for Survival Prediction in Venoarterial Extracorporeal Membrane Oxygenation
Authors: Stephens Andrew , Šeman Michael , Diehl Arne , Pilcher David , Barbaro Ryan , Brodie Daniel , Pellegrino Vincent , Kaye David , Gregory Shaun , Hodgson Carol 
Published: May 2023

Abstract
Purpose: Venoarterial extracorporeal membrane oxygenation (ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score based on a large international patient cohort.
Methods: A Deep Neural Network: ECMO Predictive Algorithm (ECMO PAL) was trained on 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017 to 2020), and performance was measured using five-fold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5,015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality.
Results: Mean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an in-hospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5 to 72.7% [99% CI: 71.1 – 74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62.0%) scores.
Conclusions: ECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies.
Different surgical treatment modalities for single-compartment knee osteoarthritis: A Bayesian network meta-analysis of randomized controlled trials
Authors: Liu Lun , Li Juebei , Wang Yunlu , Li Xiyong , Han Pengfei , Li Xiaodong 
Published: May 2023

Abstract
Background: The most prevalent chronic joint disease, knee osteoarthritis（KOA）, initially affects only the unilateral compartment and is characterized by secondary bone growth and degenerative changes in the articular cartilage. However, the surgical treatment of single-compartment knee osteoarthritis remains controversial, so we used a network meta-analysis based on Bayesian theory for total knee arthroplasty (TKA),Unicompartmental Knee Arthroplasty (UKA), and high tibia osteotomy（HTO）was evaluated for the efficacy of these three surgical techniques in the treatment of single-compartment knee osteoarthritis.
Methods: Pubmed, CNKI, The Cochrane Library databases were searched by computer. Retrieval of randomized controlled trials of TKA, UKA, and HTO for unicompartmental knee osteoarthritis, including studies comparing pairwise and three at the same time, were searched until 7 March 2023. Two independent authors were used for data extraction and literature quality evaluation, and the Knee Society Score(KSS), KSS(functional), revision rate, and complication rate were used as evaluation indicators. Stata, Revman, JAGS, and the gemtc package, which is based on R, were all used to carry out the Bayesian network meta-analysis.
Results:After screening, a total of 1438 patients from nine randomized controled trials were included in the analysis. The literature was retrieved from 2158 papers.the SUCRA value of KSS from high to low was: HTO, TKA, UKA, the SUCRA value of KSS(functional) from high to low was:HTO,UKA,TKA,the SUCRA value of the revision rate from low to high: HTO, UKA, TKA, and the SUCRA value of the complication rate from low to high was: HTO, TKA, UKA.
Conclusion: Between the three surgical approaches, there were no significant differences in postoperative KSS, KSS (functional), revision rates, or complication rates.
Systematic Review Registration:
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023420516 identifier: CRD42023420516
Assessing the sustainability of single versus multiple voucher-based family programs in Pakistan: a 24-months post-intervention evaluation
Authors: Azmat Syed Khurram , Ali Moazzam , Rahman Md. Mizanur 
Published: May 2023

Abstract
Introduction
Family planning vouchers have emerged as a promising approach to improve coverage of underserved groups or underutilized services. The current study was designed to measure the residual/longer term impact of FP voucher programs on the practices of women beyond the life of the programme.
Methods
A cross-sectional survey conducted, as part of the larger mixed-method study, approximately 24 months after the close-down of Marie Stopes Society and Greenstar Social Marketing family planning voucher intervention programmes in Punjab, Pakistan. Following necessary ethics approvals, a total of 338 voucher MSS clients & 324 voucher GSM clients were interviewed using a structured questionnaire at the household level.
Results
Compared with end-line data, a significant decrease in the modern contraceptive uptake in both MSS (90% at endline to current (or post-endline) 52%) and GSM (from 84% to current 56%) intervention sites among the voucher clients was noted. Among MSS voucher clients, the highest decline in use was observed in IUCD (54% at endline versus to current 13%); however no change between the surveys was noted among GSM clients. In both projects, following closure of voucher intervention 34% of the discontinued users in MSS and 29% in GSM sites adopted/switched to a new modern contraceptive again. In the post-intervention survey, wealth-based inequality in GSM data depicts more pro-rich utility for modern methods, indicating pro-rich inequality, in contrast, the post-intervention survey in MSS found mixed results such as pro-poor inequality for any method and modern method use.
Conclusions
The prevalence for contraception in study sites, following closure of voucher intervention remained high than national average. This study provides evidence that family planning vouchers can bring about an enduring positive change in clients’ behaviours in using modern contraceptive methods among poor populations. These results are useful to design family planning programs that will sustain when the donor funding terminates.
The Risk of Colorectal Cancer Associated with Lipid profile
Authors: Wu Jiali , Yao Zhenrong , Chen Yvrui , Lin Wenbin , Chen Dubo , Xv Hongxv , Feng Pinning , Gan Wenjia 
Published: May 2023

AbstractBackground The relationship between blood lipids and the risk of colorectal cancer (CRC) remains unclear. Methods In this study, we assessed the diagnostic performance of serum lipids in CRC patients. In this study, 206 CRC patients, 90 ulcerative colitis (UC) patients and 190 healthy controls (HC) were enrolled retrospectively. Kruskal-Wallis H test and Mann-Whitney U test were applied to compare continuous variables. Spearman analysis was used to test correlation. The independent variables for CRC were identified using univariate and multivariate logistic hazard models. Based on the results of the multivariate analysis, a prognostic nomogram was constructed. The ROC curves, calibration curves, and decision curves were used to evaluate the efficiency of the nomogram in prediction. Results The HDL-C level was significantly lower in CRC group than in HC group. The CRC group had a higher level of ApoB compared with UC group. The Lp(a) levels were significantly higher in CRC group than in HC group. Multivariate analysis results showed that ApoA1, HDL-C, ApoB and Lp(a) were independent risk factors for CRC. The nomogram created by ApoA1, HDL-C, ApoB and Lp(a) was more accurate for the prediction of CRC than each factor. The ROC curves, calibration curves and DCA curves of the nomogram showed good discriminative ability for CRC. The serum lipids were not related to TNM stage and tumor differentiation. Conclusions The risk of CRC significantly increased with decreased HDL-C and ApoA1 and increased ApoB and Lp(a). We established a nomogram for CRC prediction based on HDL-C, ApoA1, ApoB and Lp(a), which is beneficial for early diagnosis of CRC.
Cooperative Path Planning for Object Transportation with Fault Management
Authors: K Sindhuja , Sahu Bandita , Das Pardipta , Kumar Raghvendra 
Published: May 2023

Abstract
The current research intents on enhancing the service ability of mobile robot by cooperative path planning. The strategy is developed by fusion of sine cosine algorithm and particle swarm optimization approach for the transition of service robot in complex environment. To ensure the successful execution of the intended task it is essential to have a faultless and collision-less path for the mobile robot. This supposition can be achieved by producing an intelligent fault-managed approach. The proposed paper addresses the object transportation by a pair of robots from source to destination, this task can be accomplished in three step, such as fault identification, fault resolution using robot replacement and computation of a collision-less path. At each step of transition, path planning is carried out to reach the target location. Sine cosine algorithm improves the exploration capability of the robots in the multi robot environment. Particle swarm optimization being the simplest technique to exercise the path planning problem produces an optimal global position for each particle along each dimension. The fusion of both provides a balance exploration-exploitation ability of the mobile robot. Fault identification overcomes the faulty transition and unsuccessful transition of the robot by detecting the fault of any robot through its non-responding time. K- nearest neighbor approach identifies the nearest working robot to replace the faulty on. The algorithm has been exercised in C language to showcase its capability in terms of execution time, path traveled, path deviated etc. The comparative analysis proofs the supremacy of the proposed algorithm in terms of several metrics such as path planning, cooperation, fault management, etc.
Composite CYP3A (CYP3A4 and CYP3A5) phenotypes and influences on tacrolimus dose adjusted concentration in adult heart transplant recipients
Authors: Hernandez Savine , Aquilante Christina , Deininger Kimberly , Lindenfeld Joann , Schlendorf Kelly , Driest Sara Van , Liu Michelle 
Published: May 2023

Abstract
CYP3A5 genetic variants are associated with tacrolimus metabolism. Controversy remains on whether CYP3A4 increased [*1B (rs2740574), *1G (rs2242480)] and decreased function [*22 (rs35599367)] genetic variants provide additional information. This study aims to address whether tacrolimus dose-adjusted trough concentrations differ between combined CYP3A (CYP3A5 and CYP3A4) phenotype groups. Significant differences between CYP3A phenotype groups in tacrolimus dose-adjusted trough concentrations were found in the early postoperative period and continued to 6 months post-transplant. In CYP3A5 nonexpressers, carriers of CYP3A4*1B or *1G variants (Group 3) compared to CYP3A4*1/*1 (Group 2) patients were found to have lower tacrolimus dose-adjusted trough concentrations at 2 months. In addition, significant differences were found among CYP3A phenotype groups in the dose at discharge and time to therapeutic range while time in therapeutic range was not significantly different. A combined CYP3A phenotype interpretation may provide more nuanced genotype-guided TAC dosing in heart transplant recipients.
Electrohydrodynamic Convection Instabilities Observed in Suspensions of Cellulose Nanocrystals
Authors: Frka-Petesic Bruno , Jean Bruno , Heux Laurent 
Published: May 2023

Abstract
Cellulose nanocrystals (CNCs) are slender, negatively charged nanoparticles that spontaneously form a cholesteric liquid crystal in aqueous suspension above a critical concentration. When they are suspended in apolar solvents such as toluene using surfactants, the application of an AC electric field leads to the reorientation and then distortion of the cholesteric order until the cholesteric structure completely unwinds into a nematic-like order, typically above 0.4 to 0.6 kV/cm. In this work, we show that at much higher electric fields (\(\ge\)2.4 kV/cm) the sample develops a periodic pattern that varies with the field amplitude and frequency. We ascribed this pattern to electrohydrodynamic convection instabilities. These instabilities usually present complex regimes varying with the field, the voltage, the frequency and the geometry. However, the typical geometry where these instabilities were most documented across the literature differs from the geometry used in this work. This work concludes with possible future experimental investigations to clarify the exact regime of instability responsible for these observations.
Application of VR Head-Mounted Display Training System in Quantitative Evaluation of Trunk Control and Balance Function
Authors: Zhang Xu , Zhao Xueqiang , Wu Xiaoqing , Zhang Chi 
Published: May 2023

Abstract
Virtual reality (VR) technology is increasingly important in various fields, such as medicine, engineering, and science, and can be used for training and education, clinical assessment, and re- habilitation. VR is widely used in rehabilitation to evaluate and intervene in various diseases, with the potential to improve the balance and gait of patients with neurological disorders and provide additional benefits when combined with traditional rehabilitation methods. However, current VR training systems lack methods and technologies for functionally quantifying training performance. This study implemented VR head-mounted display technology and motion sensing recognition technology to achieve positioning and free movement in a virtual reality space, using motion inter- action functions in different movement modalities to achieve trunk control and balance training. Functional quantification evaluation of trunk control and balance was achieved through algorithms that include movement trajectory, trunk activity, and three-dimensional interval motion perfor- mance. The rehabilitation process of the trainees was digitally captured and updated in real-time to form a functional report, allowing medical staff and patients to understand the rehabilitation progress in real-time and promote rehabilitation effectiveness.
Unveiling the variability of Alpha Sextantis: Insights from TESS data analysis in Peranso
Authors: Hamedamen Aven M. , Murad Ismail Musa 
Published: May 2023

Abstract
This paper presents a detailed analysis of the variability of Alpha Sextantis, a non-eclipsing binary star located in the Sextans constellation. The discovery of the variability was made using data from the TESS space telescope, which captured observations over a period of several weeks. The data was then analyzed using the Peranso software, which allowed for the identification of periodic variations in the star's brightness.
The analysis revealed that the variability of Alpha Sextantis is likely due to a combination of factors, including pulsations and rotational modulation. The star's pulsation pattern was found to have a period of approximately 0.12 days, while the rotational modulation had a longer period of approximately 4.4 days.
In addition to providing new insights into the variability of Alpha Sextantis, these findings also have broader implications for our understanding of non-eclipsing binary stars more generally. By shedding light on the complex interplay between pulsations and rotational modulation, this research has the potential to inform future studies of stellar astrophysics and evolution.
A Bipartite Heterogeneous Graph Recommendation Model based on Graph Neural Network
Authors: Norton Matilda , Donovan Silas , Beckett John , Hawthorne Dashiell 
Published: May 2023

Abstract
Nowadays, big data and information technology have become mainstream, and the rapid growth of information volume has greatly met people's needs. However, the surge in data volume has also brought a negative impact on people, the most prominent problem being the influx of more and more invalid information into people's daily life. Graph data is one of the hot directions of current research in the field of data mining. A graph is an important information organization structure, which consists of nodes and edges. A large amount of data in daily life can be organized by graphs. This paper uses graphical neural network techniques to cope with the cold start problem and at the same time ensure that the model still has the ability to understand the content features of the nodes. In recommender systems, the prevalent user-item interaction graph is essentially a bifurcated heterogeneous graph, in which user nodes and item nodes belong to two different node domains, and nodes in different domains may have different initial feature dimensions. In this case, it is difficult to cope with the graph neural network for processing homogeneous graphs, so research on a bipartite heterogeneous graph inference network is needed. In this paper, we design a bipartite self-encoder based on the graph self-encoder and improve the performance of the target task through the dual-task collaborative optimization of node classification and connection prediction. In this paper, subgraph extraction is introduced to achieve diversity in training graph topology by sampling subgraphs in large graphs. Experiment results illustrate that the recommendation model in this paper achieves a performance that surpasses that of some mainstream baselines while coping with the cold start problem.