bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Subcooled MQL in cBN Hard Turning of powder-based Cr-Mo-V Tool Steel Using Simulations and Experiments
Authors: Laakso Sampsa Vili Antero , Mallipeddi Dinesh , Krajnik Peter 
Published: April 2021

Abstract
Metal cutting fluids for improved cooling and lubrication are an environmental risk and a health risk for workers. Minimizing water consumption in industry is also a goal for a more sustainable production. Therefore, metal cutting emulsions that contain hazardous additives and consume considerable amounts of water are being replaced with more sustainable metal cutting fluids and delivery systems, like vegetable oils that are delivered in small aerosol droplets, i.e. via minimum quantity lubrication (MQL). Since the volume of the cutting fluid in MQL is small, the cooling capacity of MQL is not optimal. In order to improve the cooling capacity of the MQL, the spray can be subcooled using liquid nitrogen. This paper investigates subcooled MQL with machining simulations and experiments. The simulations provide complementary information to the experiments, which would be otherwise difficult to obtain, e.g. thermal behavior in the tool-chip contact and residual strains on the workpiece surface. The cBN hard turning simulations and experiments are done for powder-based Cr-Mo-V tools steel, Uddeholm Vanadis 8 using MQL subcooled to -10 °C and regular MQL at room temperature. The cutting forces and tool wear are measured from the experiments, that are used as the calibration factor for the simulations. After calibration, the simulations are used to evaluate the thermal effects of the subcooled MQL, and the surface residual strains on the workpiece. The simulations are in good agreement with the experiments in terms of chip morphology and cutting forces. The cutting experiments and simulations show that there is only a small difference between the subcooled MQL and regular MQL regarding the wear behavior, cutting forces or process temperatures. The simulations predict substantial residual plastic strain on the workpiece surface after machining. The surface deformations are shown to have significant effect on the simulated cutting forces after the initial tool pass, an outcome that has major implications for inverse material modelling.
Numerical Analysis of Friction Conditions on Temperature and Phases Within Forged Ti-6Al-4V Aeroengine Drum
Authors: Luo Shiyuan , Jiang Yongxin , Zhang Po , Yu Fengping , Liu Siwen 
Published: April 2021

Abstract
Friction conditions significantly impact the temperature and phases of titanium forged parts, further directly affecting the microstructures and mechanical properties of final parts. In this paper, a 2D simplified FE model combined with phase transition equations is developed to simulate Ti-6Al-4V drum forging procedure. Then, friction effects on the temperature and phases of the forged drum are numerically analyzed and verified by experiments. Results indicate that unlike little influence on α+β phase, improving friction obviously decreases the general levels of temperature and β phase and increases the homogeneities of α and β phases within the forged drum, which are associated with cooling rates and the heating effects of friction and deformation.
Molecular Characterization, Expression and Functional Analysis of Yak IFITM3 Gene
Authors: Wang Haipeng , Wang Li , Li Juan , Fu Fang , Zheng Yao , Zhang Ling 
Published: April 2021

Abstract
Background IFITM3 is interferon-induced transmembrane 3, which plays an extremely key role in anti-proliferation, anti-virus and anti-tumor diseases. To expand our understanding of the role of IFITM3 in yak, this experiment studied its function. Results Firstly, the yak ( Bos grunniens ) IFITM3 ( BgIFITM3 ) gene contained a 5’-untranslated region (UTR) (25 bp), a coding region (441 bp), and a 3’-UTR (115 bp). The expression of BgIFITM3 gene in liver was significantly higher than that in heart, spleen, lung and kidney ( P <0.01). BgIFITM3 protein was localized on the yak hepatocyte membrane, and its expression level was increased first and then stabilized from 1 day to 5 years of age. Moreover, the prokaryotic expression vector of BgIFITM3 protein was constructed and expressed successfully, with a molecular weight of 19.5 kDa. Besides, the activity of yak hepatocyte was significantly inhibited after treating with BgIFITM3 protein (10 and 20 μg/mL) ( P <0.01). The expression levels of ERBB-2, IRS-1, PI3KR-1, AKT-1 and MAPK-3 were significantly lower after treating with 20 μg/mL BgIFITM3 protein ( P <0.05). Finally, the activity of HepG2 cells was significantly inhibited after treating with BgIFITM3 protein (1, 10 and 20 μg/mL) ( P <0.05). While the cloning ability and migration ability of HepG2 cells were significantly inhibited after treating with 10 μg/mL BgIFITM3 protein ( P <0.05). The mitochondria of HepG2 cells were concentrated, cristae widened, and the double film density of mitochondria was increased after treating with 10 μg/mL BgIFITM3 protein. After 10 μg/mL BgIFITM3 protein treating, the expression levels of VDAC-2, VDAC-3 , p53 genes were significantly increased, but the expression level of GPX-4 gene was significantly decreased ( P <0.01). Conclusion Taken together, the BgIFITM3 protein could inhibit the proliferations of yak hepatocyte and HepG2 cells by regulating the PI3K/Akt pathway or ferroptosis-related genes, respectively. These results benefit for further study of the function of BgIFITM3 protein.
Implementation of a Community-Based Low-Calorie Dietary Intervention for the Induction of Type-2 Diabetes and Pre-Diabetes Remission: A Feasibility Study Utilising a Type-2 Hybrid Design
Authors: Quimby Kim , Sobers Natasha , George Colette , Greaves Natalie , Browman-Jones Francine , Samuels T Alafia 
Published: April 2021

Abstract
Objectives

The aims of this feasibility study were to (1) examine the implementation of a community-based health advocate (CHA) training program to develop the clinical skills needed to support a diabetes remission protocol based on a low-calorie diet (LCD) and (2) investigate if participant weight loss can be achieved and diabetes remission induced under these conditions.
Methods

This tripartite study followed a type-2 implementation-effectiveness design. Three faith-based organizations (FBOs) were purposively selected as study sites. Implementation outcomes were guided by the Consolidated Framework for Implementation Research. During the pre-implementation phase, site ‘readiness’ to facilitate the intervention was determined from a site visit and an interview with the FBOs’ leadership. During the implementation phase, congregants could volunteer for the 10-week CHA training which included practical exercises in weight, glucose and blood pressure (BP) measurement, and a summative practical assessment. Acceptability and implementation effectiveness were assessed via survey. During the intervention phase, other congregants and community members with T2DM or pre-diabetes and overweight were invited to participate in the 12-week LCD. Anti-diabetic medication was discontinued on day-1 of the intervention. Clinical effectiveness was determined from the change in weight, fasting blood glucose (FBG) and BP which were monitored weekly at the FBO by the CHA. HbA1C was performed at week 1 and 12.
Results

The FBOs were found to be ready as determined by their adequate resources and engagement in health-related matters. Twenty-nine CHAs completed the training; all attained a passing grade at ≥ 1 clinical station, indicating implementation effectiveness. CHA feedback indicated that the program structure was acceptable and provided sufficient access to intervention-related material. Thirty-one persons participated in the LCD (11 T2DM:20 pre-diabetes). Mean (95%CI) weight loss was 6.8kgs (5.4–8.2) p < 0.00001; 7.9kgs in males vs 6.6kgs in females; A1C decreased from 6.6–6.2%, p = 0.003 and FBG from 6.4-6.0mmol/L; p = 0.004. T2DM remission rates were 60% and 90% by A1C < 6.5% and FBG < 7mmol/L respectively. Pre-diabetes remission was 18% and 40% by A1C < 5.7% and FBG < 5.6 respectively.
Conclusion

Implementation of a community-based diabetes remission protocol is both feasible and clinically effective. Its sustainability is to be determined. Adaptability to other disorders or other settings should be investigated.
Trial Registration
: NCT03536377 registered 24th May 2018 at https://clinicaltrials.gov/ct2/show/NCT03536377
Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder
Authors: Michelassi Gabriel C. , Bortoletti Henrique S. , Pinheiro Tuany D. , Nobayashi Thiago , Barros Fabio R. D. , Testa Rafael L. , Silva Andréia F. , Revers Mirian C. , Portolese Joana , Pedrini Helio , Brentani Helena , Nunes Fatima L. S. , Machado-Lima Ariane 
Published: April 2021

Abstract
Autism Spectrum Disorder (ASD) is a developmental disorder that persistently impairs communication and social interaction, with a high degree of clinical heterogeneity. The fact that the diagnosis is based on clinical observation which requires therapy expertise, interviews with parents and application of questionnaires, it contributes to delaying the diagnosis until school age. The study of anthropometric measures in individuals with ASD and individuals in typical development (TD) showed there are some differences between the two groups. However, there is a lack of computational tools to assist in the acquisition and analysis process of these measures. This paper proposes a computer-aided medical decision support system that, given a child’s facial image captured by a digital camera, can discriminate between the two groups, ASD and TD, thus helping in the diagnosis. We defined a protocol for image acquisition and preprocessing, tested and compared methods for dataset balancing, dimension reduction and classification. The best results were obtained by a SVM classifier with 86.2% accuracy. Once the proposed model is based on facial images, it has the potential to facilitate early diagnosis of ASD.
Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions
Authors: Zhou Jingru , Zhuang Yingping , Xia Jianye 
Published: April 2021

Abstract
BackgroundGenome-scale Metabolic Model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.ResultsBased on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale B values, predicting the differential expression of enzymes under different growth conditions. ConclusionsThis study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of Aspergillus niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.
Identification of RRM2 as a Potential Biomarker for the Diagnosis and Prognosis of Rheumatoid Arthritis
Authors: Biao Wu , Chen Yufeng , Zhong Junlong , Zhong Shuping , Wang Bin , Li Xuegang , Jiang Ning , Shang Jie , Xu Xianghe , Lu Huading 
Published: April 2021

Abstract
Background: Rheumatoid arthritis (RA) is a common autoimmune disease that can occur at any age. If treatment is delayed, RA can seriously affect the patients’ quality of life. However, there is no diagnostic criteria for RA and the positive predictive value of the current biomarkers is moderate. Objective: to identify RA-associated susceptibility genes and explore their potential as a novel biomarker for diagnosis and evaluation of the prognosis of RA.Methods: Peripheral blood mononuclear cells (PBMCs) were collected from healthy human donors and RA patients. RNA-seq analyses were performed to identify the differentially expressed genes (DEGs) between RA and control samples. The PBMCs-mRNA in DEGs were further subjected to enrichment analysis. Furthermore, the hub genes and key modules associated with RA were screened by bioinformatics analyses. Then, the expression of hub genes in RA were assessed in mRNA expression profiles. Next, real time-quantitative PCR (RT-qPCR) analyses were performed to further confirm the expression of the hub genes from the PBMCs that collected from 47 patients with RA and 40 healthy controls. Finally, we evaluated the clinical characters for the candidate mRNAs.Results: RNA-seq analyses revealed the expression of 178 mRNAs from PBMCs were disregulated between the healthy controls and the RA patients. Bioinformatics analyses revealed 10 hub mRNAs. The top 3 significant functional modules screened from PPI network functionally were involved in DNA replication origin binding, chemokine activity, etc. After validating the 10 hub mRNAs in GSE93272 dataset and clinical samples, we identified 3 candidate mRNAs, including ASPM, DTL and RRM2. Among which, RRM2 showed great capacity in discriminating between remissive RA and active RA. Significant correlations were observed between DTL and IL-8, TNF-α, between RRM2 and CDAI, DAS-28, tender joints and swollen joints, respectively. The AUC values of ASPM, DTL and RRM2 were 0.654, 0.995 and 0.990, respectively.Conclusion: We successfully identified multiple candidate mRNAs associated with RA. RRM2 showed high diagnosis efficiency with the AUC of 0.990 (sensitivity=100%, specificity=97.5%). And RRM2 severed as an additional biomarker for evaluating disease activity. The findings provided a novel candidate biomarker for diagnosis and evaluation of the prognosis of RA.
Increasing Medicinal and Phytochemical Compounds of Coneflower (Echinacea Purpurea L.) Medicinal Plant using New Hydroponic Culture Media and Nutrition Pattern
Authors: Ahmadi Fatemeh , Samadi Abbas , Sepehr Ebrahim , Rahimi Amir , Shabala Sergey 
Published: April 2021

Abstract
Medicinal plants are considered as one of the most important sources of chemical compounds, so preparing a suitable culture media for medicinal plant growth is a critical factor. The present study is aimed to improve the caffeic acid derivatives and alkylamides percentages of Echinacea purpurea root extract by optimizing the NO3-/NH4 + ratio in new hydroponic culture media. Perlite particle size in the growing media was varied as very coarse perlite (more than 2 mm), coarse perlite (1.5-2 mm), medium perlite (1-1.5 mm), fine perlite (0.5-1 mm), and very fine perlite (less than 0.5 mm) with a mixture of peat moss at 50:50 v/v and 30:70 v/v peat moss to perlite ratios. Two NO3-/NH4 + ratios (90:10 and 70:30) were tested in each growing media. All phytochemical analyses were performed according to standard methods using high performance liquid chromatography (HPLC). It was found that the E. purpurea grown in the medium containing very fine-grade perlite with 50:50 v/v perlite to peat moss ratio had the maximum caffeic acid derivatives, including chicoric acid (17 mg/g DW), caftaric acid (6.3 mg/g DW), chlorogenic acid (0.93 mg/g DW), cynarin (0.84 mg/g DW), and echinacoside (0.73 mg/g DW), as well as, alkylamides (54.21%). The percentages of these phytochemical compounds increased by decreasing perlite particle size and increasing of NO3-/NH4 + ratio. The major alkylamide in the E. purpurea root extract was dodeca-2E, 4E, 8Z-10 (E/Z)-tetraenoic acid isobutylamide in all treatments, ranging from 31.12% to 54.21% of total dry weight. It can be concluded that optimizing hydroponic culture media and nutrient solution has significant effects on E. purpurea chemical compounds.
TPGen: Prescription Generation Using Knowledge-guided Translator
Authors: Ruan Chunyang , Luo Hua , Wu Yingpei , Yang Yun 
Published: April 2021

Abstract
Background: Prescriptions contain a lot of clinical information and play a pivotal role in the clinical diagnosis of Traditional Chinese Medicine (TCM), which is a combination of herb to treat the symptoms of a patient from decision-making of doctors. In the process of clinical decision-making, a large number of prescriptions have been invented and accumulated based on TCM theories. Mining complex and the regular relationships between symptoms and herbs in the prescriptions are important for both clinical practice and novel prescription development. Previous work used several machine learning methods to discover regularities and generate prescriptions but rarely used TCM knowledge to guide prescription generation and described why each herb is predicted for treating a symptom. Methods: In this work, we employ a machine translation mechanism and propose a novel sequence-to-sequence (seq2seq) architecture termed TPGen to generate prescriptions. TPGen consisting of an encoder and a decoder is a well-known framework for resolving the machine translation problem in the natural language processing (NLP) domain. We use the lite transformer and Bi-directional Gate Recurrent Units(Bi-GRUS) as a fundamental model in TPGen, and integrate TCM clinical knowledge to guide the model improvement termed TPGen+. Results: We conduct extensive experiments on a public TCM dataset and clinical data. The experimental results demonstrate that our proposed model is effective and outperforms other state-of-the-art methods in TCM expert evaluation. The approach will be beneficial for clinical prescription discovery and diagnosis
Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure
Authors: Rahman Faisal , Finkelstein Noam , Alyakin Anton , Gilotra Nisha , Trost Jeff , Schulman Steven , Saria Suchi 
Published: April 2021

Abstract
Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecific symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS.Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identification approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were first deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team. Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. Future studies need to evaluate if CS analysis of high-risk cohort identification may affect outcomes.
Identification and documentation of Ethno-veterinary remedies used by Afar Pastoralists for the treatment of Camel diseases in Ethiopia
Authors: Gutema Fikadu , G.Aregawi Weldegebrial , Bekele Juhar Tesfaye , Geletu Abel sorsa 
Published: April 2021

Abstract
Background: Ethnoveterinary medicine is a scientific term for traditional animal health care that encompasses knowledge, belief, practice and skill of the community used for curing diseases and maintaining health of animals. A cross-sectional study was conducted using semi-structured questionnaires from September 2016 to August 2017 to gather information on the knowledge, practice and skill of the pastoralists and traditional healers about the importance and application of Ethno-veterinary medicine. Moreover, this study was conducted with the objectives of identification and documentation of major ethno-veterinary medicinal plants commonly used to treat camel aliments in Afar region.Result: A total of 146 traditional healers and pastoralists were interviewed for baseline and reliable information about Ethno-veterinary medicinal practice in Afar region. Accordingly, 99.3% of the respondents were men in which 66.44% of them were in the age range of 45-60. Majorities (76.02%) of the information’s concerning EVM were obtained from the elders among which 84.93% responded that the overall EVM practice and status in pastoral society of Afar is decreasing. A total of 710 plant species supposed to have medicinal property were recommended by pastoralists to be used for the treatment of 49 different animal diseases. Among 64 plants specimen collected, Balanites aegyptia, Cissus rotundifolia (Forssk.) Vahl, Cadaba rotundifolia Forssk, Solanum sp, Acalypha indica L and Tamarix aphylla (L.) Karsts were the commonly used plant species with high fidelity value. Concerned with plant parts used, leaf (20.5%) was the most frequently used plant parts followed by root (19.4%), fruit (14.4%), seed (10.8%) and entire plant (8.8%). These herbal remedies were administered orally, nasally, ocular and topically.Conclusion: pastoralists and traditional healers in Afar region have immense indigenous knowledge and practices of treating sick animals which should be supported by scientific techniques for analyzing the active ingredients contained in each plant species and for evaluating the safety, efficacy and dosage of the commonly used medicinal plants. In addition, on station establishment of commonly used medicinal plant nursery for conservation purpose and to conduct in-depth phytochemical analysis is paramount important.