bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy
Authors: Mirsanaye Kamdin , Uribe Castaño Leonardo , Kamaliddin Yasmeen , Golaraei Ahmad , Augulis Renaldas , Kontenis Lukas , Done Susan J. , Žurauskas Edvardas , Stambolic Vuk , Wilson Brian C. , Barzda Virginijus 
Published: June 2022

AbstractThe extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained (H&E) tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor from normal tissue, resulting in 94.2% for both accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.
Comparing cross-sectional and longitudinal tracking to establish percentile data and assess performance progression in swimmers
Authors: Born Dennis-Peter , Rüeger Eva , Beaven C. Martyn , Romann Michael 
Published: June 2022

AbstractTo provide percentile curves for short-course swimming events, including 5 swimming strokes, 6 race distances, and both sexes, as well as to compare differences in race times between cross-sectional analysis and longitudinal tracking, a total of 31,645,621 race times of male and female swimmers were analyzed. Two percentile datasets were established from individual swimmers’ annual best times and a two-way analysis of variance (ANOVA) was used to determine differences between cross-sectional analysis and longitudinal tracking. A software-based percentile calculator was provided to extract the exact percentile for a given race time. Longitudinal tracking reduced the number of annual best times that were included in the percentiles by 98.35% to 262,071 and showed faster mean race times (P < 0.05) compared to the cross-sectional analysis. This difference was found in the lower percentiles (1st to 20th) across all age categories (P < 0.05); however, in the upper percentiles (80th to 99th), longitudinal tracking showed faster race times during early and late junior age only (P < 0.05), after which race times approximated cross-sectional tracking. The percentile calculator provides quick and easy data access to facilitate practical application of percentiles in training or competition. Longitudinal tracking that accounts for drop-out may predict performance progression towards elite age, particularly for high-performance swimmers.
Genistein induces long-term expression of progesterone receptor regardless of estrogen receptor status and improves the prognosis of endometrial cancer patients
Authors: Yoriki Kaori , Mori Taisuke , Aoyama Kohei , Tarumi Yosuke , Kataoka Hisashi , Kokabu Tetsuya , Kitawaki Jo 
Published: June 2022

AbstractProgesterone is used to treat uterine endometrial cancer in young patients wishing to preserve their fertility as well as in advanced or recurrent patients, but its response rate is limited. The antitumor effect of progesterone is mediated by progesterone receptor (PR) binding. Hence, loss of progesterone’s therapeutic effect, i.e., development of progesterone resistance, is mainly due to decreased PR expression. However, little is known about underlying mechanisms that regulate PR expression. Immunohistochemistry analysis of specimens from 31 young, endometrial cancer patients showed that elevated PR expression significantly increased (P < 0.05) rates of progression-free and overall survival. We investigated mechanisms of regulating PR expression and suppressing cell proliferation using genistein, a chemotherapeutic agent against different cancers. Genistein inhibits cell growth by inducing cell cycle arrest in G2 and apoptosis; moreover, it upregulates prolonged expression of PR-B and forkhead box protein O1, regardless of estrogen receptor alpha expression in endometrial cancer cells. Genistein-induced PR expression decreases CCAAT/enhancer binding protein beta expression and activates c-Jun N-terminal kinase pathway, rather than causing epigenetic alterations of the PR promoter. Therefore, increased PR expression is an important antitumor effect of genistein. This may help to improve the response rates of fertility-sparing treatments for young patients.
Health status of recreational runners over 10-km up to ultra-marathon distance based on data of the NURMI Study Step 2
Authors: Wirnitzer Katharina , Boldt Patrick , Wirnitzer Gerold , Leitzmann Claus , Tanous Derrick , Motevalli Mohamad , Rosemann Thomas , Knechtle Beat 
Published: June 2022

AbstractEndurance running is well-documented to affect health beneficially. However, data are still conflicting in terms of which race distance is associated with the maximum health effects to be obtained. Therefore, the aim of this study was to compare the health status of endurance runners over different race distances. A total of 245 recreational runners (141 females, 104 males) completed an online survey. Health status was assessed by measuring eight dimensions in two clusters of health-related indicators (e.g., body weight, mental health, chronic diseases and hypersensitivity reactions, medication intake) and health-related behaviors (e.g., smoking habits, supplement intake, food choice, healthcare utilization). Each dimension consisted of analytical parameters derived to a general domain score between 0 and 1. Data analysis was performed by using non-parametric ANOVA and MANOVA. There were 89 half-marathon (HM), 65 marathon/ultra-marathon (M/UM), and 91 10-km runners. 10-km runners were leaner than both the HM and M/UM runners (p ≤ 0.05). HM runners had higher health scores for six dimensions (body weight, mental health, chronic diseases and hypersensitivity reactions, medication intake, smoking habits, and health care utilization), which contributed to an average score of 77.1% (score range 62–88%) for their overall state of health. Whereas 10-km and M/UM runners had lesser but similar average scores in the overall state of health (71.7% and 72%, respectively). Race distance had a significant association with the dimension “chronic diseases and hypersensitivity reactions” (p ≤ 0.05). Despite the null significant associations between race distance and seven (out of eight) multi-item health dimensions, a tendency towards better health status (assessed by domain scores of health) among HM runners was found compared to other distance runners. However, the optimal state of health across all race distances supported the notion that endurance running contributed to overall health and well-being.Trial registration number: ISRCTN73074080. Retrospectively registered 12th June 2015.
Metabolomics-based profiling for quality assessment and revealing the impact of drying of Turmeric (Curcuma longa L.)
Authors: Salem Mohamed A. , El-Shiekh Riham A. , Fernie Alisdair R. , Alseekh Saleh , Zayed Ahmed 
Published: June 2022

AbstractTurmeric, the rhizomes of Curcuma longa L., is one of the top selling spices, food preservatives, and food colorants. In addition, it exhibits health promoting benefits owing to its unique phytochemical composition. Nevertheless, it is commonly subjected to heat drying, hence, the dried powder is the most used form and can easily be adulterated with allied species. Therefore, our research aimed to profile the phytochemical composition and investigate the impact of drying of turmeric. Extraction and fractionation followed by LC- and GC–MS analysis resulted in the identification of a total of 161 metabolites belonged to various phytochemical classes. Moreover, multivariate data analysis identified curcuminoids, terpecurcumins, and organic acids as potential markers for drying. Based on the applied analytical techniques in combination with chemometrics, these investigations have succeeded to provide good coverage of the metabolome of turmeric in both fresh and dried forms.
Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS)
Authors: Boktor Marian , Ecclestone Benjamin R. , Pekar Vlad , Dinakaran Deepak , Mackey John R. , Fieguth Paul , Haji Reza Parsin 
Published: June 2022

AbstractHistopathological visualizations are a pillar of modern medicine and biological research. Surgical oncology relies exclusively on post-operative histology to determine definitive surgical success and guide adjuvant treatments. The current histology workflow is based on bright-field microscopic assessment of histochemical stained tissues and has some major limitations. For example, the preparation of stained specimens for brightfield assessment requires lengthy sample processing, delaying interventions for days or even weeks. Therefore, there is a pressing need for improved histopathology methods. In this paper, we present a deep-learning-based approach for virtual label-free histochemical staining of total-absorption photoacoustic remote sensing (TA-PARS) images of unstained tissue. TA-PARS provides an array of directly measured label-free contrasts such as scattering and total absorption (radiative and non-radiative), ideal for developing H&E colorizations without the need to infer arbitrary tissue structures. We use a Pix2Pix generative adversarial network to develop visualizations analogous to H&E staining from label-free TA-PARS images. Thin sections of human skin tissue were first virtually stained with the TA-PARS, then were chemically stained with H&E producing a one-to-one comparison between the virtual and chemical staining. The one-to-one matched virtually- and chemically- stained images exhibit high concordance validating the digital colorization of the TA-PARS images against the gold standard H&E. TA-PARS images were reviewed by four dermatologic pathologists who confirmed they are of diagnostic quality, and that resolution, contrast, and color permitted interpretation as if they were H&E. The presented approach paves the way for the development of TA-PARS slide-free histological imaging, which promises to dramatically reduce the time from specimen resection to histological imaging.
Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes
Authors: Asadzadeh Shiva , Yousefi Rezaii Tohid , Beheshti Soosan , Meshgini Saeed 
Published: June 2022

AbstractDue to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli–Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1–2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.
Relating SARS-CoV-2 variants using cellular automata imaging
Authors: Souza Luryane F. , Rocha Filho Tarcísio M. , Moret Marcelo A. 
Published: June 2022

AbstractWe classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.
Formulation process, physical stability and herbicidal activities of Cymbopogon nardus essential oil-based nanoemulsion
Authors: Somala Naphat , Laosinwattana Chamroon , Teerarak Montinee 
Published: June 2022

AbstractEssential oil-based bioherbicides are a promising avenue for the development of eco-friendly pesticides. This study formulated nanoemulsions containing citronella (Cymbopogon nardus) essential oil (CEO) as an herbicidal product using a high-pressure homogenization method with hydrophilic-lipophilic balance (HLB) values ranging 9–14.9 for the surfactant mixture (Tween 60 and Span 60). The CEO was high in monoterpene compounds (36.333% geraniol, 17.881% trans-citral, 15.276% cis-citral, 8.991% citronellal, and 4.991% β-citronellol). The nanoemulsion at HLB 14 was selected as optimal due to having the smallest particle size (79 nm, PI 0.286), confirmed by transmission electron microscopy. After 28 days of storage, particle size in the selected formulation changed to 58 and 140 nm under 4 °C and 25 °C, respectively. Germination and seedling growth assays with Echinochloa crus-galli showed that the nanoemulsion exerted a significant dose-dependent inhibitory effect at all tested HLBs (9–14.9) and concentrations (100–800 µL/L). The inhibitory effect was greatest at HLB 14. Treatment of E. cruss-galli seed with the HLB 14 nanoemulsion significantly reduced seed imbibition and α-amylase activity. Our findings support that CEO nanoemulsions have a phytotoxic effect and hence herbicidal properties for controlling E. cruss-galli. Accordingly, this nanoemulsion may have potential as a bioherbicide resource.
Five-year outcomes of one anastomosis gastric bypass as conversional surgery following sleeve gastrectomy for weight loss failure
Authors: Kermansaravi Mohammad , Karami Reza , Valizadeh Rohollah , Rokhgireh Samaneh , Kabir Ali , Pakaneh Mohammadali , Kassir Radwan , Pazouki Abdolreza 
Published: June 2022

AbstractThe most accepted procedures as conversion for poor weight changes after sleeve gastrectomy (SG), are malabsorptive surgeries. This study was designed to evaluate the 5-year outcomes of One Anastomosis Gastric Bypass (OAGB) following SG due to weight loss failure and weight regain. From September 2014 to January 2017, totally 23 patients with a history of SG conversion to OAGB in terms of weight loss failure or weight regain who had completed their 5-year follow-ups were studied. Some obesity related co-morbidities containing type-2 diabetes (DM), hypertension (HTN), dyslipidemia, obstructive sleep apnea (OSA) and gastroesophageal reflux disease (GERD) were also investigated at 1, 2, 3 and 5 years after conversional surgery. All cases had remission/improvement in DM, DLP, HTN and OSA 1 year after conversional OAGB. Analysis showed statistically significant (P < 0.001) change in trend of BMI. Mean BMI before conversional surgery, at 1, 2, 3and 5 years were 46.3 ± 10.4, 34.5 ± 8.5, 34.1 ± 8.6, 35.7 ± 8.7 and 37.5 ± 11.6, respectively. Mean percent excess weight loss (%EWL) at 1, 2, 3 and 5 years was 51.6 ± 11.0, 52.9 ± 13.1, 45.5 ± 16.4 and 41.0 ± 18.0, respectively. Mean percent total weight loss (%TWL) at 1, 2, 3 and 5 years was 26.6 ± 5.9, 27.4 ± 7.2, 23.9 ± 9.2 and 20.9 ± 9.3, respectively. OAGB is an effective conversional procedure for insufficient weight loss and weight regain following failed SG and lead to satisfactory changes in obesity associated medical problems. The optimal weight loss results are obtained at 2-year follow-ups and these effects are then reduced.
Childhood temperament and adulthood personality differentially predict life outcomes
Authors: Wright Amanda J. , Jackson Joshua J. 
Published: June 2022

AbstractDebate has long surrounded whether temperament and personality are distinct sets of individual differences or are rather two sides of the same coin. To the extent that there are differences, it could indicate important developmental insights concerning the mechanisms responsible for linking traits with outcomes. One way to test this is to examine the joint and incremental predictive validity of temperament and personality in the same individuals across time. Using a longitudinal sample spanning 3 decades starting at infancy and followed up to 37 years old (N = 7081), we ran a series of Bayesian generalized linear models with measures of childhood temperament and adult-based personality to predict outcomes in several life domains. Results indicated that while each set of individual differences were often related to the same outcomes, there were instances in which temperament provided incremental validity above adult personality, ranging from 2 to 10% additional variance explained. Personality in childhood explained the most variance for outcomes such as cognitive ability and educational attainment whereas personality performed best for outcomes such as health status, substance use, and most internalizing outcomes. These findings indicate childhood and adulthood assessments of personality are not redundant and that a lifespan approach is needed to understand fully understand life outcomes.
Validating S‐parameter measurements of RF integrated circuits at milli‐Kelvin temperatures
Authors: Stanley M. , Parker‐Jervis R. , de Graaf S. , Lindström T. , Cunningham J. E. , Ridler N. M. 
Published: June 2022

AbstractTechniques to precisely characterise RF components at milli‐Kelvin temperatures support the development of quantum computing systems utilising these components. In this work, an S‐parameter measurement setup to characterise RF integrated circuits at milli‐Kelvin temperatures has been proposed and for the first time, the S‐parameter measurements at milli‐Kelvin temperatures have been validated using two independent calibration techniques, thereby providing more confidence in measurements. The techniques are demonstrated experimentally by comparing and validating calibrated S‐parameter measurements of a cryogenic attenuator integrated circuits at milli‐Kelvin temperatures.
Cognitive selection of driving automation levels in highly automated vehicles leveraging on Bayesian networking principles
Authors: Panagiotopoulos Ilias , Dimitrakopoulos George 
Published: June 2022

AbstractHighly automated vehicles (HAVs) are expected to fundamentally change the on‐road transportation field by increasing traffic flow efficiency, reducing road crashes caused by human errors, and increasing comfort and driving productivity. With the help of recent developments in cognitive management techniques and machine learning analysis, intelligent on‐board computing services are gaining acceptance. The main goal of the present paper is to introduce and develop a novel on‐board cognitive decision‐making functionality that dynamically and automatically enables HAVs to operate each time in the best available level of driving automation (LoDA). The proposed functionality utilizes several attributes associated with the driving environment and the driver's personality characteristics and personal preferences. The cognitive nature of the proposed functionality is based on previous knowledge, turned into experience, by implementing the Naive Bayes classifier supervised machine learning method. The effectiveness of the proposed cognitive functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal LoDA, is illustrated by modelling and analysing three scenarios with regards to drivers with different profile data and to driving scenes with different environment characteristics. Therefore, it can operate as an in‐car intelligent personal assistant for the drivers.
Joint 2D‐DOA and polarization estimation for Electromagnetic vector sensors array with compressive measurements
Authors: Zhu Dongchen , Cheng Xiancheng , Chen Baixiao 
Published: June 2022

AbstractElectromagnetic vector sensors (EMVS) arrays have been employed extensively in the field of array signal processing for their advantage in terms of polarization diversity. However, with the introduction of EMVS, the cost of the hardware equipment and the complexity of the corresponding parameter estimation algorithms increase considerably, as the number of received signal channels and the dimension of the received signal are much larger than the traditional scalar array. In order to effectively reduce hardware cost and algorithm complexity, we propose a scheme that combines an electromagnetic vector sensor array with a compression network. We construct the corresponding signal model and based on this we derive a Compressed Reduced Dimensional MUltiple SIgnal Classification (Compressed To avoid the multi‐dimensional search, Reduced Dimension MUSIC) algorithm which can effectively reduce the computational complexity. While selecting the coefficient matrix of the compressed network, random selection can cause information loss, which leads to the performance degradation of the estimation algorithm. To address this problem, we propose an optimization method for coefficient matrix selection based on the maximum signal‐to‐noise ratio (SNR) criterion. Numerical simulations are conducted in different scenarios to verify the effectiveness of the parameter estimation algorithm and the optimization algorithm.
Recognition and parameter estimation of spaceborne synthetic aperture radar pulse repetition interval modulation based on short time modified pulse repetition interval transform
Authors: Qiao Guanyu , Dai Dahai , Zhang Caikun , Ji Penghui , Pang Bo 
Published: June 2022

AbstractThe traditional pulse repetition interval (PRI) transform method and modified PRI transform method have a low ability to recognise the complex PRI modulation type adopted in spaceborne synthetic aperture radar systems. To solve this problem, a short‐time modified PRI transform (STMPT) method based on the traditional PRI transform method was proposed. The STMPT method converts the one‐dimensional representation of PRI estimation results into a time‐varying characteristic sensitive (TVCS) matrix of the PRI. Combined with the mathematical models of the four PRI modulation types including PRI fixed, staggered PRI, fast PRI change, and a more elaborated PRI sequence concatenating multiple fast PRI change sequences, the non‐zero segment characteristics were obtained by incoherent accumulation and rearrangement of the TVCS matrix. Parameter estimation of the four PRI modulation types was also realised based on the geometrical and statistical characteristics. Finally, the effectiveness and performance of the algorithm were verified by simulation analysis.