bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing
Authors: Jeon Hyeon-Ju , Lim Hae Gyun , Shung K. Kirk , Lee O-Joun , Kim Min Gon 
Published: November 2022

AbstractThis study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.
Climate suitability of the Mediterranean Basin for citrus black spot disease (Phyllosticta citricarpa) based on a generic infection model
Authors: Galvañ Anaïs , Boughalleb-M’Hamdi Naima , Benfradj Najwa , Mannai Sabrine , Lázaro Elena , Vicent Antonio 
Published: November 2022

AbstractCitrus black spot (CBS), caused by the fungus Phyllosticta citricarpa, is associated with serious yield and quality losses. The climate suitability of the Mediterranean Basin for CBS development has been long debated. However, CBS has been described in Tunisia. In this study, a generic model was used to simulate potential infections by ascospores and pycnidiospores together with a degree-day model to predict the onset of ascospore release. High-resolution climatic data were retrieved from the ERA5-Land dataset for the citrus-growing regions in the Mediterranean Basin and other locations where CBS is present. In general, the onset of ascospore release was predicted to occur late in spring, but there is no agreement on the adequacy of this empirical model for extrapolation to the Mediterranean Basin. The generic model indicated that infections by ascospores and pycnidiospores would be concentrated mainly in autumn, as well as in spring for pycnidiospores. In contrast to previous studies, the percentage of hours suitable for infection was higher for pycnidiospores than for ascospores. The values obtained with the generic infection model for Tunisia and several CBS-affected locations worldwide were similar to those for other citrus-growing regions in Europe and Northern Africa. These results support previous work indicating that the climate of the Mediterranean Basin is suitable for CBS development.
Chemical composition, antibacterial efficacy, and antioxidant capacity of essential oil and oleoresin from Monodora myristica and Tetrapleura tetraptera in Southeast Nigeria
Authors: Okechukwu Queency N. , Ugwuona Fabian U. , Ofoedu Chigozie E. , Juchniewicz Szymon , Okpala Charles Odilichukwu R. 
Published: November 2022

AbstractSpecific to the West African sub-region, previous studies involving fruit, stem, and bark of Tetrapleura tetraptera as well as seeds of Monodora myristica have largely focused on phytochemical properties of aqueous and methanolic and ethanolic extracts. To supplement existing information, the chemical composition, antibacterial efficacy (tested against Escherichia coli and Staphylococcus aureus), and antioxidant capacity (1,1-diphenyl-2-picrylhydrazyl (DPPH∙) radical scavenging, ferric reducing power, and total antioxidant capacity) of essential oil and oleoresin extracted from T. tetraptera fruit and M. myristica seeds cultivated in Southeast Nigeria, were studied. Essential oil and oleoresin were respectively extracted by steam distillation and aqueous maceration. By way of gas chromatograph mass spectrometry (GC–MS) analysis, the chemical compounds from essential oil and oleoresin from M. myristica and T. Tetraptera samples totaled 6 and 5, as well as 27 and 16, respectively. Besides the oleoresin of M. myristica and the essential oil of T. tetraptera showing some resistance against S. aureus, the oleoresins seemed highly susceptible to E. coli—all of which demonstrated concentration-dependence to the antibacterial inhibition zone. Scavenging DPPH radical, reduction power activity, and total antioxidant capacity increased with essential oil and oleoresin extracts' concentrations, which positions M. myristica and T. tetraptera spices as very promising for food preservation, especially against autoxidation and microbial spoilage.
Effect of intake manifold geometry on cylinder-to-cylinder variation and tumble enhancement in gasoline direct injection engine
Authors: Shin Jisoo , Kim Donghwan , Son Yousang , Park Sungwook 
Published: November 2022

AbstractIn this study, the effect of intake manifold geometry on cylinder-to-cylinder variation was investigated considering the volumetric efficiency, early tumble development, turbulent kinetic energy, and spark plug gap velocity using computational fluid dynamic program, CONVERGE v2.4. The simulation model was validated based on the PIV experiment in the cylinder and Mie-scattering experiment of intake manifold, and its results agreed well with the experiment results. The curved intake manifold and straight manifold were compared. As a result, it was advantageous for cylinder-to-cylinder variation in the straight intake manifold compared to the curved intake manifold in perspective of volumetric efficiency which were a maximum deviation of 1.7% in curved manifold and 0.6% in straight manifold. And the straight manifold had an effect of the strengthening the in-cylinder flow, so that the turbulent kinetic energy near TDC was increased to maximum 11% than curved manifold. And considering the effect of manifold curve radius on in-cylinder flow intensity in straight manifold, with increasing engine speed, the in-cylinder flow intensified during compression with decreasing the intake manifold radius due to the short distance between manifold inlet and port. Especially at 2000 rpm, the tumble ratio increased 55% at intake manifold radius of 10 cm than of 7 cm at bTDC 280 deg. Therefore, for the purpose of enhancing the in-cylinder flow near spark plug timing, shortened distance between intake manifold inlet and port and increasing the manifold radius is required.
Fast-acting and injectable cryoneurolysis device
Authors: Moradi Tuchayi Sara , Wang Ying , Khodorova Alla , Pence Isaac J. , Stemmer-Rachamimov Anat , Evans Conor L. , Anderson R. Rox , Garibyan Lilit 
Published: November 2022

AbstractCryoneurolysis is an opioid-sparing therapy for long-lasting and reversible reduction of pain. We developed a nerve-selective method for cryoneurolysis by local injection of ice-slurry (− 5 to − 6 °C) that induced decrease in nocifensive response starting from about a week after treatment and lasting up to 8 weeks. In this study, we test the hypothesis that injection of colder slurry leads to faster onset of analgesia. Colder slurry (− 9ºC) was injected around the rat sciatic nerve to induce cryoneurolysis. Hematoxylin and Eosin (H&E) staining was used to examine histologic effects on surrounding tissues. Coherent anti-Stokes Raman scattering (CARS) microscopy was used to study effects on myelin sheaths. Functional tests were used to assess changes in sensory and motor function in the treated hind paw. No inflammation or scarring was detected in surrounding skin and muscle tissues at day 7 post slurry injection. Functional tests showed rapid onset reduction in mechanical pain sensitivity starting from day 1 and lasting up to day 98. CARS imaging demonstrated disintegration of myelin sheaths post treatment followed by complete recovery of nerve structure by day 140. In this study we showed that colder slurry (− 9 °C) produces more rapid onset and longer duration of analgesia, while remaining nerve-selective
Low-cost quasi-global optimization of expensive electromagnetic simulation models by inverse surrogates and response features
Authors: Koziel Slawomir , Pietrenko-Dabrowska Anna 
Published: November 2022

AbstractConceptual design of contemporary high-frequency structures is typically followed by a careful tuning of their parameters, predominantly the geometry ones. The process aims at improving the relevant performance figures, and may be quite expensive. The reason is that conventional design methods, e.g., based on analytical or equivalent network models, often only yield rough initial designs. This is especially the case for miniaturized components featuring considerable electromagnetic (EM) cross couplings, or antenna systems with non-negligible radiator coupling (e.g., MIMO, closely-spaced arrays). For reliability reasons, parametric optimization is carried out using EM simulation tools, which is a time-consuming task. In many cases, designer needs to resort to a global search, especially when handling several objectives and constraints is necessary, or the high-frequency structure under design is overly complex. Combination of both aforementioned factors makes it no longer possible to rely on engineering insight, even to detect a promising region of the design space. Unfortunately, nature-inspired algorithms, commonly employed for solving these tasks typically exhibit significant computational expenditures. This paper proposes a simple yet efficient method for globalized search using a response feature approach and inverse regression surrogates. Owing to less nonlinear dependence of the feature point coordinates on the system variables (as compared to the original responses, e.g.,S-parameter frequency characteristics), our methodology permits a rapid identification of the most appropriate regions of the parametric space, and further design tuning by means of local routines. At the same time, the overall optimization cost is comparable to the cost of local procedures. The proposed approach is validated using several high-frequency structures (a dual-band antenna, a microstrip coupler, an impedance matching transformer) optimized under different design scenarios. Global search capability and computational efficiency are demonstrated through comprehensive comparisons with multiple-start local search, as well as particle swarm optimizer, a representative nature-inspired algorithm.
Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images
Authors: Chang Runsheng , Qi Shouliang , Wu Yanan , Song Qiyuan , Yue Yong , Zhang Xiaoye , Guan Yubao , Qian Wei 
Published: November 2022

AbstractThe individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images. Two datasets of NSCLC patients treated with chemotherapy as the first-line treatment were collected from two hospitals. Dataset 1 (163 response and 138 nonresponse) was used to train, validate, and test the DMIL model and dataset 2 (22 response and 20 nonresponse) was used as the external validation cohort. Five backbone networks in the feature extraction module and three pooling methods were compared. The DMIL with a pre-trained VGG16 backbone and an attention mechanism pooling performed the best, with an accuracy of 0.883 and area under the curve (AUC) of 0.982 on Dataset 1. While using max pooling and convolutional pooling, the AUC was 0.958 and 0.931, respectively. In Dataset 2, the best DMIL model produced an accuracy of 0.833 and AUC of 0.940. Deep learning models based on the MIL can predict chemotherapy response in NSCLC using pretreatment CT images and the pre-trained VGG16 with attention mechanism pooling yielded better predictions.
Hypertensive disorders of pregnancy share common cfDNA methylation profiles
Authors: Spinelli Marialuigia , Zdanowicz Jarmila A. , Keller Irene , Nicholson Pamela , Raio Luigi , Amylidi-Mohr Sofia , Mosimann Beatrice , Surbek Daniel , Mueller Martin 
Published: November 2022

AbstractHypertensive disorders of pregnancy (HDP) contribute substantially to perinatal morbidity and mortality. Epigenetic changes point towards cardio-metabolic dysregulation for these vascular disorders. In early pregnancy, epigenetic changes using cell free DNA (cfDNA) are largely unexplored. We aimed to investigate these in HDP between 11 and 14 weeks of gestation by analysis of cfDNA methylation profiles in patients with hypertensive disorders. We identified patients without chronic hypertension but with subsequent development of preeclampsia (PE) (n = 11), with chronic hypertension (HT) but without PE development (n = 14), and lacking both PE and HT (n = 422). We matched patients according to PE risk factors into three groups (n = 5 each group): (1) PE: no HT but PE development, (2) HT: chronic hypertension but no PE and (3) Control: no PE or HT. We successfully optimized our cfDNA isolation process prior to whole genome bisulfite sequencing. Analysis of cfDNA methylation changes indicate a common predisposition in PE and HT groups, chiefly of maternal origin. Assessment of significant differentially methylated regions and annotated genes point towards a common cardiovascular predisposition in preeclampsia and hypertension groups in the first trimester. We postulate the pivotal role of the maternal cardiovascular system in HDP, which is already evident in the first trimester.
Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes
Authors: Nazzicari Nelson , Biscarini Filippo 
Published: November 2022

AbstractDeep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genome predictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results. While most works focus on exploring alternative network architectures, in this study we propose an innovative representation of marker genotype data and tested it against the GBLUP (Genomic BLUP) benchmark with linear and nonlinear phenotypes. From publicly available cattle SNP genotype data, different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscale slices are extracted and fed to a deep convolutional neural network (DNN). We simulated nine phenotype scenarios with combinations of additivity, dominance and epistasis, and compared the DNN to GBLUP-A (computed using only the additive kinship matrix) and GBLUP-optim (additive, dominance, and epistasis kinship matrices, as needed). Results varied depending on the accuracy metric employed, with DNN performing better in terms of root mean squared error (1–12% lower than GBLUP-A; 1–9% lower than GBLUP-optim) but worse in terms of Pearson’s correlation (0.505 for DNN compared to 0.672 and 0.669 of GBLUP-A and GBLUP-optim for fully additive case; 0.274 for DNN, 0.279 for GBLUP-A, and 0.477 for GBLUP-optim for fully dominant case). The proposed approach offers a basis to explore further the application of DNN to tabular data in whole-genome predictions.
Synthesis of guanidinophosphazene superbase for the facile preparation of high molecular weight polysiloxane under mild conditions
Authors: Duan Wensheng , Li Zhongwei , Yang Zhizhou , He Fuyan , Chen Shisen , Qiao Congde , Yao Jinshui , Zhang Changbin , Zhao Hui , Li Mei , Yang Guihua 
Published: November 2022

Novel guanidinophosphazene superbase (HTGCP) was successfully prepared only in one step. The facile preparation of polysiloxane with high conversion and precisely controllable Mn using superbase at room temperature in a short time.
Retracted: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm
Authors: Cao Yu , Xu Gaofeng , Gao You , Song Changxin 
Published: November 2022

AbstractThe above article from IET Communications, published online on 17 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Magnetic field analytical calculation method for radial unite of a combined magnetic bearing
Authors: Ren Xiaojun , Feng Ming , Ren Tianming 
Published: November 2022

AbstractCombined magnetic bearing with radial and axial unites is an important supporting element for high speed and high energy density motor. Accurate calculation of the magnetic field is a key to design and optimise a Combined Radial and Axial Magnetic Bearing (CRAMB). This study focuses on magnetic field analytical calculation method for radial Unite of CRAMB. The analytical calculation model of magnetic field is based on subdomain method. The Laplace's equations in the air‐gap subdomain are solved by the variable separation method. The magnetic field of magnetic bearing with large diameter rotor is analysed and calculated in Cartesian coordinate system. Considering the influence of curvature the magnetic field distribution of magnetic bearing with small diameter rotor is deduced and calculated analytically in polar coordinate system. The effectiveness of the analytical method is verified by finite element and experimental results.
Scope and Limitations of the Palladium-Catalyzed Direct 1,2-Diheteroarylation of 1,2-Dihalobenzene Derivatives
Authors: Doucet Henri , Liu Linhao 
Published: November 2022

AbstractThe reactivity of 1,2-dihalobenzenes in palladium-catalyzed C–H bond functionalization is more difficult to control than that of 1,3- and 1,4-dihalobenzenes because of a possible Pd 1,4-migration during the second catalytic cycle. With C3-substituted heteroarenes this 1,4-palladium migration is not possible and the reaction allows the synthesis of a wide variety of functionalized 1,2-heteroarylated benzene derivatives in high yields. Conversely, only a few heteroarenes with a free C3 position, such as thiazoles, furans and imidazoles, allow the preparation of 1,2-heteroarylated benzenes. Despite these limitations, this one-pot procedure employing an easily available catalyst [Pd(OAc)2] and an inexpensive base (KOAc) provides a very simple method for the preparation of several 1,2-heteroarylated benzenes.
Auditory Mirror Therapy for Tinnitus: A Pilot Study
Authors: Linnman Clas 
Published: November 2022

Abstract
Background Tinnitus, the phantom perception of sound, shares many properties with phantom limb pain, in that both may arise as a consequence of sensory deprivation. Prediction errors in multisensory integration, such as induced with mirror box therapy in phantom pain, can reduce phantom percepts.
Purpose We evaluated if a device that swaps sound from the right pinna to the left ear canal, and from the left pinna to the right ear canal, can reduce tinnitus. We call this auditory mirror therapy (AMT).
Research Design Uncontrolled trial.
Study Sample Twenty subjects with chronic tinnitus.
Intervention An AMT device consisting of a modified ear defender device with microphones that swaps sounds from left pinna to the right ear canal and from the right pinna to the left ear canal. Participants used the device at home for 2 weeks.
Data Collection and Analysis Tinnitus Handicap Inventory (THI), Tinnitus Reaction Questionnaire, and visual analog scale ratings of symptoms were collected at baseline and posttreatment. Repeated measures t-test were performed, Bonferroni corrected for multiple comparisons.
Results There was a significant reduction in THI and in awareness of tinnitus after the AMT intervention.
Conclusion Exposing the audiovisual integration system to prediction errors can help retrain phantom percepts and reduce tinnitus handicap. Further studies on this concept are warranted.
Knee Arthroscopy Prior to Total Knee Arthroplasty: Temporal Relationship to Surgical Complications
Authors: Sax Oliver C. , Bains Sandeep S. , Chen Zhongming , Salib Christopher G. , Nace James , Delanois Ronald E. 
Published: November 2022

AbstractMechanical knee symptoms secondary to knee osteoarthritis (OA) may warrant knee arthroscopy (KA). Degenerative changes may progress and require a subsequent total knee arthroplasty (TKA). Recent studies have evaluated the timing of KA prior to TKA, associated a narrow interval with increased post-TKA complications. However, an updated analysis is required. We compared surgical outcomes in recipients of KA prior to TKA as stratified by four, time-dependent cohorts (< 12, 12 to 16, 16 to 20, and 20 to 24 weeks prior to TKA). We specifically compared: 90-day (1) manipulations under anesthesia (MUAs); (2) septic revisions at 90 days, 1 year, and 2 years; as well as (3) how various independent risk factors influenced the manipulations or revisions. We queried a national database for patients undergoing TKA. Patients who underwent KA with the following intervals: < 12 (n = 1,023), 12 to 16 (n = 816), 16 to 20 (n = 1,957), and 20 to 24 weeks (1,727) prior to TKA were compared with those patients who did not have a history of KA (n = 5,000). Bivariate analyses were utilized to assess 90 days through 2 years surgical outcomes. Afterwards, multivariate regression models were utilized to assess for independent risk factors. The unadjusted analyses showed an increase in MUA through 2 years among all the KA recipients (p < 0.001). Septic revisions and surgical site infections at all time points were not associated with any of the four arthroscopy time intervals (p > 0.476). The adjusted analyses showed an increased risk for 90-day MUA among all TKA cohorts (p < 0.001). Risk for septic revisions did not reach significance. Delaying TKA by 24 weeks in KA recipients was not associated with a decreased risk in septic revisions. However, there was an apparent risk of MUA at 90 days for all KA cohorts suggesting that waiting after KA before TKA makes no difference in MUA rates. These results update existing literature, may serve as an adjunct to current practice guidelines, and can contribute to shared decision making in the preoperative setting.