bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Effective LSSVM-Based Approach for Milling Tool Wear Prediction
Authors: Ge Yingshang , Zhang Jianhua , Song Guohao , Zhu Kangyi 
Published: July 2022

Abstract
In order to realize real-time and precise monitoring of the tool wear in the milling process, this paper presents a tool wear predictive model based on the Stacked Multilayer Denoising AutoEncoders (SMDAE) technique, the particle swarm optimization with an adaptive learning strategy (PSO-ALS) and the Least Squares Support Vector Machine (LSSVM). Cutting force and vibration information are adopted as the monitoring signals. The unique feature extraction and fusion method consists of three steps: multi-domain features extraction, dimension-reduction by principal component analysis (PCA) and dimension-increment by SMDAE. As a novel feature representation learning approach, the SMDAE technique is utilized to fuse the PCA-based fusion features to enrich the effective information by realizing dimension-increment, thus helping polish up the predictive performance of the proposed model. PSO-ALS is used to obtain the optimal parameters for LSSVM, simplifying the problem and increasing the population diversity. Twelve sets of cutting experiments are conducted to verify the effectiveness of the proposed model. The experimental results show that the presented model is superior to models such as PSO-LSSVM in predictive performance, and the SMDAE technique effectively improves the prediction accuracy of the constructed model. The findings of this paper provide theoretical guidance for monitoring milling tool wear in real industrial situations.
Carleman linearization approach for chemical kinetics integration toward quantum computation
Authors: Akiba Takaki , Morii Youhi , Maruta Kaoru 
Published: July 2022

Abstract
The Harrow, Hassidim, Lloyd (HHL) algorithm is a quantum algorithm expected to accelerate solving large-scale linear ordinary differential equations (ODEs). To apply the HHL to non-linear problems such as chemical reactions, the system must be linearized. In this study, Carleman linearization was utilized to transform nonlinear first-order ODEs of chemical reactions into linear ODEs. Although this linearization theoretically requires the generation of an infinite matrix, the original nonlinear equations can be reconstructed. For the practical use, the linearized system should be truncated with finite size and analysis precision can be determined by the extent of the truncation. Matrix should be sufficiently large so that the precision is satisfied because quantum computers can treat. Our method was applied to a one-variable nonlinear system to investigate the effect of truncation orders in Carleman linearization and time step size on the absolute error. Subsequently, two zero-dimensional homogeneous ignition problems for H2–air and CH4–air gas mixtures were solved. The results revealed that the proposed method could accurately reproduce reference data. Furthermore, an increase in the truncation order in Carleman linearization improved accuracy even with a large time-step size. Thus, our approach can provide accurate numerical simulations rapidly for complex combustion systems.
Have The Cake And Eat It Too: Differential Privacy Enables Privacy And Precise Analytics
Authors: Subramanian Rishabh 
Published: July 2022

Abstract
Existing research in differential privacy, whose applications have exploded across functional areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset and its utility for analytics. Resolving this trade-off critically impacts potential applications of differential privacy to protect privacy in datasets even while enabling analytics using them. In contrast to the existing literature, this paper shows how differential privacy can be employed to precisely—not approximately—retrieve the analytics on the original dataset.We examine, conceptually and empirically, the impact of noise addition on the quality of data analytics. We show that the accuracy of analytics following noise addition increases with the privacy budget and the variance of the independent variable. Also, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual data to which we add Laplace noise, we provide evidence supporting these two predictions. We then demonstrate our central thesis that, once the privacy budget employed for differential privacy is declared and certain conditions for noise addition are satisfied, the slope parameters in the original dataset can be accurately retrieved using the estimates in the modified dataset of the variance of the independent variable and the slope parameter. Thus, differential privacy can enable robust privacy as well as precise data analytics.
Disparities and Determinants of Food Insecurity for New Mexico’s Unique Populations
Authors: Gutierrez Katherine M 
Published: July 2022

Abstract
New Mexico, a majority-minority state, had the second-highest poverty rate in the US (19.5%) and highest rate of food insecurity (16.8%) in 2019. Its unique history means that nearly half of Hispanics in the state are from families that can trace their ancestral arrival back to the late 1500s, an understudied subgroup called Hispaños (old New Mexican families). I use the Current Population Survey Food Security Supplement for the state of New Mexico from 2000-2018 to look at food insecurity by Hispanic origin and nativity attempting to isolate Hispaño households from other Hispanic origin households. Using linear probability models, results show that foreign- and native-born Hispanics were more likely to experience food insecurity than non-Hispanic New Mexicans of any nativity. Within those groups, and Hispaños are less likely than their Mexican-origin peers to be food insecure. These results emphasize the importance of data disaggregation, and the results show promising results for further investigation into the outcomes of Hispanics with long-standing ties to an area versus those who are more recent arrivals.
Analysis of Applications of the Rice Blast Disease-Resistance Pita-R Gene and Development of Pita-R-dCAPS Marker for Efficient Marker-Assisted Selection
Authors: He Niqing , Huang Fenghuang , Yu Mingxiang , Zhu Yebao , Li Qingshun Q. , Yang Dewei 
Published: July 2022

Abstract
Rice blast is a detrimental rice disease caused by the fungus Magnaporthe oryzae. Here, we identified a resistance gene from the rice cultivar Fuhui 2663 which is resistant to the rice blast isolate KJ201. Through isolated population analyses and sequencing approaches, the candidate gene was traced to chromosome 12. With the use of a map-based cloning strategy, the resistance gene was ultimately mapped to an 80-kb resistance locus region containing the Pita gene. Candidate gene prediction and cDNA sequencing indicated that the target resistance gene in Fuhui 2663 was allelic to Pita, thus being referred to as Pita-R hereafter. Further analysis showed that the Pita-R protein had one amino acid change: Ala (A) residue 918 in Pita-R was replaced by Ser (S) in Pita-S, leading to a significant change in the 3D structure of the Pita-S protein. CRISPR/Cas9 knockout experiments confirmed that Pita-R is responsible for the resistance phenotype of Fuhui 2663. Importantly, Pita-R did not affect the main agronomic traits of the variety compared to the Pita gene as verified by knockout experiments, indicative of potential applications of Pita-R in broader breeding programs. Furthermore, a Pita-R-dCAPS molecular marker with good specificity and high efficiency was developed to facilitate rice breeding for resistance to this devastating disease.
Evaluation of edge computing platforms through TinyML workloads
Authors: Morales-García Juan , Bueno-Crespo Andrés , Martínez-España Raquel , Posadas Juan-Luis , Manzoni Pietro , Cecilia José M. 
Published: July 2022

Abstract
The Internet of Things (IoT) enables the next economic revolution in which data and immediacy are the key players. Edge computing is a compelling alternative for enabling computing capabilities at the network's edge. These computing capabilities could help transform the generated data into useful information by executing machine learning (ML) workloads. TinyML is emerging as a fast-growing ML ecosystem field aiming to perform workloads on typically battery-operated devices, providing sensor data analytics at extremely low power (mW). In this work, we evaluate a wide range of TinyML and edge computing platforms to assess the computational/energy trade-off of these platforms. In this work, we analyse the Arduino Nano 33 BLE Sense microcontroller and three different edge computing devices, namely the CPU-based Raspberry Pi 4 and the CPU-GPU Nvidia Jetson platforms Nano and AGX Xavier. We run two lightweight artificial neural networks (ANN) to forecast the internal temperature of an operational greenhouse. Our results show that the microcontroller-based devices can offer a competitive and energy-efficient computational alternative to more traditional edge computing approaches for lightweight ML workloads.
Tiliroside suppresses Triple-negative breast cancer as a multifunctional CAXII inhibitor
Authors: Han Rui , Yang Hongxing , Ling Changquan , Lu Lingeng 
Published: July 2022

Abstract
Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by poor prognosis, early recurrence, and the lack of durable chemotherapy responses and specific targeted treatments. In this preclinical study, we examines Tiliroside (TS, C30H26O13), as one of the major compounds of Tribulus terrestris L. which has been used as alternative therapy in clinic practice of breast cancer treatment, for its therapeutic use in TNBC. The association between CAXII expression level and survival probability of TNBC patients, and the difference of CAXII expression level between TNBC and normal samples were evaluated by using public databases. To determine the anticancer efficacy of TS on TNBC cells, cell proliferation, wound healing, cell invasion, and 3D spheroid formation assays were performed and excellent anticancer activities of TS were displayed. Mouse models further demonstrate that TS significantly reduces the tumor burden and improves survival rate. The properties of TS as a novel CAXII inhibitor have also been evaluated by CAXII activity assay, pHi, pHe and lactate level assay. Further RT-PCR and Caspase-3 activity analyses also revealed the positive regulating effects of TS on E2F1,3/Caspase-3 axis in TNBC cells cultured in 2D or 3D systems. The findings indicate that TS suppresses TNBC progression as a potential novel CAXII inhibitor in preclinical experiments, which warrants further investigation on its therapeutic implications.
Corneal higher order aberrations by Sirius topography and their relation to different refractive errors
Authors: Salman Abdelrahman , Kailani Obeda , Ghabra Marwan , Omran Rana , Darwish Taym R , Shaaban Rafea , Ibrahim Hussein , Alhaji Hala , Khalil Hussam 
Published: July 2022

Abstract
Purpose: to compare the root mean square (RMS) of anterior corneal higher-order aberrations (HOAs) in patients with refractive errors.Methods: This retrospective observational study was conducted at Department of Ophthalmology, Tishreen University Hospital, Latakia, Syria. The eyes were divided into four groups based on refractive error: mild-to-moderate myopia, hypermetropia, myopic astigmatism, and emmetropic eyes as controls. The following anterior corneal HOAs were evaluated using the Scheimpflug-Placido Sirius (CSO, Italy) topographer over 6 mm pupil: Root mean square (RMS) total corneal HOAs, RMS trefoil, RMS coma and RMS spherical aberrations. Results: RMS values of total HOAs, trefoil and coma showed a statistically significant difference in all four groups (P < 0.05, all). They were lowest in the control group (0.18± 0.09, 011± 0.08 and 0.09± 0.08 μm, respectively) and highest in the myopic astigmatism group (0.31± 0.16, 0.15± 0.12, 0.17± 0.14 μm, respectively). RMS spherical aberration was lowest in the astigmatism group (0.00± 0.16 μm) with a statistically significant difference from that in the control groups (0.05± 0.07 μm, P = 0.049).Conclusion: The mean RMS values of total HOAs, trefoil and coma were highest in the astigmatism group and lowest in the control group. However, spherical aberration was minimal in the astigmatism group.
Physics-Informed Reinforcement Learning for Motion Control of a Fish-like Swimming Robot
Authors: Rodwell Colin , Tallapragada Phanindra 
Published: July 2022

Abstract
Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and un-modelled governing physics of the fluid-robot interaction. Commonly used low-fidelity control models using simplified formulas for drag and lift force do not capture key physics that can play an important role in the dynamics of small-sized robots with limited actuation. Deep Reinforcement Learning (DRL) holds considerable promise for motion control of robots with complex dynamics. Reinforcement learning methods require large amounts of data exploring a large subset of the relevant state space, which can be expensive, time consuming or unsafe to obtain. Data from simulations can be used in the initial stages of DRL, but in the case of swimming robots, large numbers of simulations are also infeasible from a time and computational resources perspective. Surrogate models that capture the primary physics of the system can be a useful starting point for training a DRL agent which is subsequently transferred to train with a higher fidelity simulation. We demonstrate the utility of such physics-informed reinforcement learning to train a policy that can enable velocity and path tracking for a planar swimming (fish-like) rigid Joukowski hydrofoil. This is done through a curriculum where the DRL agent is first trained to track limit cycles in a velocity space for a representative nonholonomic system and then transferred to to train on a small simulation data set of the swimmer. The results show the utility of physics-informed reinforcement learning for the control of fish-like swimming robots.
Phase I study: safety and efficacy of ex vivo-expanded allogeneic natural killer cells (MG4101) with rituximab for relapsed/refractory B-cell non-Hodgkin lymphoma
Authors: Kim Won Seog , Yoon Dok Hyun , Koh Youngil , Jung Miyoung , Kwak Jeong-Eun , Shin Eui-Cheol , Hwang Yu K. 
Published: July 2022

Abstract
MG4101, an ex vivo-expanded allogeneic natural killer cell, can enhance rituximab antibody-dependent cytotoxicity in relapsed/refractory B-cell non-Hodgkin lymphoma (r/rNHL). Its safety and efficacy were assessed in this phase I trial. Patients received escalating doses of intravenous MG4101 every 2 weeks plus rituximab. Interleukin-2 was administered subcutaneously after MG4101 treatment. Fludarabine plus cyclophosphamide was administered intravenously before rituximab in cycles 1, 3, and 5. A 3 + 3 design was used to determine the maximum tolerated dose (MTD) and maximum feasible dose (MFD). Assessments were performed over a six-cycle period, with an extended maintenance period of up to eight cycles. Nine patients received three different doses of MG4101 and rituximab. MTD could not be determined because of the absence of dose-limiting toxicity. Treatment-related adverse events, mostly grade 1 or 2, occurred in 89% patients. Only one patient experienced grade 1 cytokine release syndrome. MG4101 persisted for at least 7 days in seven patients. Four patients achieved a partial response, and one attained a complete response, yielding a 55.6% response rate. Two patients showed prolonged responses and low exhaustion marker levels in T cells. MG4101 plus rituximab presented a favorable safety profile and overall response rate in patients with r/rNHL.
Antimicrobial activity of ceftobiprole and comparator agents when tested against Gram-positive and -negative organisms collected across China (2016-2018)
Authors: yin dandan , Shi Wu , Yang Yang , Yonggui Zheng , Zhu Demei , Yan Guo , hu fupin 
Published: July 2022

Abstract
Background: Ceftobiprole is a fifth-generation cephalosporin which has been reported on the antibacterial spectrum when against bacteria collected from other countries except China. This study evaluated the in vitro activity of ceftobiprole in comparison with other comparators against clinically significant isolates collected across from China. Results: Susceptibility testing of ceftobiprole and comparators against 1163 clinically isolated Gram-positive and Gram-negative bacteria was performed with broth micro dilution method following the CLSI guidelines. All 110 S. aureus were susceptible to ceftobiprole with MIC50/90 of 1/2 mg/L for MRSA and 0.5/1 mg/L for MSSA. For Coagulase-negative staphylococci (CNS), MIC50/90 of ceftobiprole for MRCNS and MSCNS was 1/2 mg/L and 0.25/0.5 mg/L. Ceftobiprole demonstrated good potency against E. faecalis (MIC50/90 of 0.5/1 mg/L) but bad against E. faecium (MIC50/90 of >32/>32 mg/L). Ceftobiprole demonstrated potent activity against all 39 β-hemolytic Streptococcus with MIC50/90 ≤0.015/≤0.015-2 mg/L and 110 of PSSP with 98.2% susceptibility. Ceftobiprole inhibited all isolates of H. influenzae and M. catarrhalis at ≤ 1 mg/L. 91.8% and 98.2% of the ESBL-negative E. coli and K. pneumoniae were susceptible to ceftobiprole, but most of the ESBL-positive or carbapenem-resistant strains were also resistant to ceftobiprole. Ceftobiprole inhibited 84.2% of carbapenem-susceptible P. aeruginosa and 94.1% of carbapenem-susceptible A. baumannii at ≤8 mg/L, but only 52.6% of carbapenem-resistant P. aeruginosa and 5.3% of carbapenem-resistant A. baumannii. Conclusion: Ceftobiprole was active in vitro against a broad range of clinically relevant contemporary Gram-positive and Gram-negative bacterial isolates.
Utilization of Mean Platelet Volume (MPV) to Predict Perfusion Defects in Myocardial SPECT in Diabetic and Non-Diabetic Patients
Authors: Arefnia Maryam , Ariani Toktam Hosseinnezhad , Ghodsirad Mohammadali , alirezaei Toktam , Hosseinzade Elinaz , Amoui Mahasti , pirayesh Elahe , Norouzi Ghazal 
Published: July 2022

Abstract
Introduction
; An increase in the average volume of platelets has been associated with poor outcomes in patients with acute coronary syndrome. The aim of this study was to assess the use of myocardial perfusion scans to study the association between MPV and myocardial perfusion abnormalities in patients with and without diabetes mellitus (DM). diabetic and non-diabetic patients.
Materials and Methods
; This was a cross-sectional study. Forty-nine patients with known type 2 diabetes DM and without obvious cardiovascular symptoms and 49 healthy individuals were included. A myocardial perfusion scan was performed in rest and stress with dipyridamole/dobutamine/exercise testing. Data entered SPSS software and analyzed. A p value below 0.05 was considered as statistically significant.
Results;
In total, 98 participants including 49 patients with DM and 49 healthy individuals were included. Laboratory variables as well as cardiac scan items including Summed Stress Score (SSS), Summed Rest Score (SRS), Summed Difference Score (SDS), Ejection Fraction (EF) and End Systolic Volume (ESV) were not statistically different in the two groups. Only the average platelet count was higher in controls than diabetics. In regression analysis, by one-unit increase in MPV, the SRS increased by an average of 0.46 (CI; 0.08–0.83 β: 0.46), only in healthy controls.
Discussion and Conclusion
; MPV is a predictor of myocardial perfusion defects, which could be measured easily for patients in different clinical scenarios as a herald for coronary artery disease, especially in healthy individuals.
Sufentanil combined with parecoxib sodium inhibits breast cancer proliferation and metastasis via regulating epithelial-mesenchymal transition in HER2-positive breast cancer cells
Authors: Xu Sunmian , Li Xiaohui , Li Wenbin , Ma Nina , Ma Hongjun , Cui Jian , You Xiaogang , Chen Xuexin 
Published: July 2022

Abstract
Background: Sufentanil combined with parecoxib sodium is a commonly used combination of postoperative anesthetic analgesics in perioperative oncology patients. However, the effects on HER2-positive breast cancer cells are still unknown. The aim of this study was to investigate the effects and potential mechanisms of sufentanil combined with parecoxib sodium on HER2-positive breast cancer cells.Methods: Cell proliferation, cycle, migration and invasion abilities were assessed by CCK-8 assay, plate clone formation assay, flow cytometric cycle assay, scratch and transwell invasion assays, and Western blot assay was used to detect the expression of proteins involved in cell cycle, migration, invasion and epithelial-mesenchymal transition (EMT)-related proteins. The effect of tumor growth and metastasis in vivo was examined by constructing an orthotopic transplantation model of HER2-positive breast cancer in mice.Results: Functional assays indicated that sufentanil combined with parecoxib sodium induced blockade of HER2-positive breast cancer cells BT474 in the G1 phase of the cell cycle and inhibited cell proliferation, migration and invasion in vitro. Western blot assays revealed that sufentanil combined with parecoxib sodium down-regulated the expression of Cyclin D1, MMP-9, MMP-2 and VEGFA proteins in BT474 cells, as well as the expression of mesenchymal phenotypic proteins N-cadherin, Vimentin and Snail, while upregulated the expression of epithelial phenotypic protein E-cadherin. In addition, we found that sufentanil combined with parecoxib sodium inhibited tumor growth and metastasis in a recipient mice model.Conclusion: Sufentanil combined with parecoxib sodium inhibits HER2-positive breast cancer disease progression, including cell proliferation, cycle, migration, and invasion, and this alteration is associated with EMT.