Miracle-dz/UniFAR-FTUs huggingface dataset:
Wave reflection quantification analysis and personalized flow wave estimation based on the central aortic pressure waveform

Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for monitoring cardiovascular status. Accurate quantification of pressure wave reflections requires the measurement of aortic flow wave. However, direct flow measurement involves extra equipment and well-trained operator. In this study, the personalized aortic flow waveform was estimated from the individual central aortic pressure waveform (CAPW) based on pressure-flow relations. The separated forward and backward pressure waves were used to calculate wave reflection indices such as reflection index (RI) and reflection magnitude (RM), as well as the central aortic pulse transit time (PTT). The effectiveness and feasibility of the method were validated by a set of clinical data (13 participants) and the Nektar1D Pulse Wave Database (4,374 subjects). The performance of the proposed personalized flow waveform method was compared with the traditional triangular flow waveform method and the recently proposed lognormal flow waveform method by statistical analyses. Results show that the root mean square error calculated by the personalized flow waveform approach is smaller than that of the typical triangular and lognormal flow methods, and the correlation coefficient with the measured flow waveform is higher. The estimated personalized flow waveform based on the characteristics of the CAPW can estimate wave reflection indices more accurately than the other two methods. The proposed personalized flow waveform method can be potentially used as a convenient alternative for the measurement of aortic flow waveform.
Estimation of Body Weight Based on Biometric Measurements by Using Random Forest Regression, Support Vector Regression and CART Algorithms

Simple Summary This study aimed to estimate body weight from various biometric measurements and features such as genotype (share of Suffolk and Polish Merino genotypes), birth weight (BiW), sex, birth type and body weight at 12 months of age (LBW) and some body measurements such as withers height (WH), sacrum height (SH), chest depth (CD), chest width (CW), chest circumference (CC), shoulder width (SW) and rump width (RW). Three hundred and forty-four animals were used in the study. Data mining and machine learning algorithms such as Random Forest Regression, Support Vector Regression and classification and regression tree were used to estimate the body weight from various features. Results show that the random forest procedure may help breeders improve characteristics of great importance. In this way, the breeders can get an elite population and determine which features are essential for estimating the body weight of the herd in Poland. Abstract The study’s main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study estimated the capabilities of CART, support vector regression and random forest regression algorithms. To compare the estimation performances of the evaluated algorithms and determine the best model for estimating body weight, various body measurements and sex and birth type characteristics were assessed. Data from 344 sheep were used to estimate the body weights. The root means square error, standard deviation ratio, Pearson’s correlation coefficient, mean absolute percentage error, coefficient of determination and Akaike’s information criterion were used to assess the algorithms. A random forest regression algorithm may help breeders obtain a unique Polish Merino Suffolk cross population that would increase meat production.
Metabolomic Diversity of Human Milk Cells over the Course of Lactation—A Preliminary Study

Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC–QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM.
Mapping study of papillary thyroid carcinoma in China: Predicting EQ-5D-5L utility values from FACT-H&N

Objective To develop a mapping algorithm that can be used to predict EQ-5D-5L health utility scores from FACT-H&N and obtain health utility parameters for Chinese patients with papillary thyroid carcinoma (PTC), which can be used for cost-utility analysis in health economic. Methods A total of 1,050 patients with PTC from a tertiary hospital in China were included, and they completed FACT-H&N and EQ-5D-5L. Four mapping algorithms of direct mapping functions were used to derive the models: Ordinary least squares (OLS), Tobit model (Tobit), Two-part model (TPM), and Beta mixture regression model (Beta). The goodness-of-fit of models was assessed by the mean absolute error (MAE), root mean square error (RMSE), Akaike information criteria (AIC), Bayesian information criteria (BIC), and absolute error (AE). A fivefold cross-validation method was used to test the stability of the models. Results The mean utility value of the EQ-5D-5L was 0.870 ± 0.094. The mean EQ-VAS score was 76.5 ± 13.0. The Beta mixture regression model mapping FACT-H&N to EQ-5D-5L achieved the best performance [fivefold cross-validation MAE = 0.04612, RMSE = 0.06829, AIC = −2480.538, BIC = −2381.137, AE > 0.05 (%) = 32.48, AE > 0.1 (%) = 8.95]. The independent variables in this model were Physical Well-Being (PWB), Emotional Well-Being (EWB), Head & Neck Cancer Subscale (HNCS) scores and its square term and interaction term scores. Conclusions This study calculated the health utility score of Chinese patients with PTC. The reported algorithms can be used to map the FACT-H&N into the EQ-5D-5L, which can be applied in the cost-utility related study of patients with PTC.
Uncertainty bounds for parameter identification with small sample sizes

Consider the problem of determining uncertainty bounds for an M-estimate of a parameter vector from (generally) non-i.i. d. data (M-estimates are those obtained as the solution of a set of equations; maximum likelihood estimates are perhaps the most common type). Calculating uncertainty bounds requires information about the distribution of the estimate. It is well known that M-estimates typically have an asymptotic normal distribution. However, because of their generally complex nonlinear (and implicitly defined) structure, very little is usually known about the finite-sample distribution. This paper presents a method for characterizing the distribution of an M-estimate when the sample size is small. The approach works by comparing the actual (unknown) distribution of the estimate with a closely related known distribution. Some discussion and analysis are included that compare the approach here with the well-known bootstrap and saddlepoint methods. Theoretical justification and an illustration of the approach in a signal-plus-noise estimation problem are presented. This illustrative problem arises in many contexts, including random effects modeling ("unbalanced" case), the problem of combining several independent estimates, Kalman filter-based modeling, small area survey analysis, and quantile calculation for projectile accuracy analysis.
An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation

Image-based methods have been applied to support structural monitoring, product and material testing, and quality control. Lately, deep learning for compute vision is the trend, requiring large and labelled datasets for training and validation, which is often difficult to obtain. The use of synthetic datasets is often applying for data augmentation in different fields. An architecture based on computer vision was proposed to measure strain during prestressing in CFRP laminates. The contact-free architecture was fed by synthetic image datasets and benchmarked for machine learning and deep learning algorithms. The use of these data for monitoring real applications will contribute towards spreading the new monitoring approach, increasing the quality control of the material and application procedure, as well as structural safety. In this paper, the best architecture was validated during experimental tests, to evaluate the performance in real applications from pre-trained synthetic data. The results demonstrate that the architecture implemented enables estimating intermediate strain values, i.e., within the range of training dataset values, but it does not allow for estimating strain values outside those range. The architecture allowed for estimating the strain in real images with an error ∼0.5%, higher than that obtained with synthetic images. Finally, it was not possible to estimate the strain in real cases from the training performed with the synthetic dataset.
Simultaneous Localization and Calibration (SLAC) Methods for a Train-Mounted Magnetometer

and calibration (SLAC) algorithm based on a Rao-Blackwellized particle filter that enables magnetic train localization using only uncalibrated magnetometer measurements. In this paper, a lower-complexity version of the SLAC algorithm is proposed that only estimates a subset of calibration parameters. An evaluation compares the full and reduced SLAC approach to a particle filter in which the magnetometer is pre-calibrated with a fixed set of parameters. The results show a clear advantage for both SLAC approaches and that the SLAC algorithm with a reduced set of calibration parameters achieves the same performance as the one with a full set of parameters.
Biceps Tenodesis Better Improves the Shoulder Function Compared with Tenotomy for Long Head of the Biceps Tendon Lesions: A Meta-Analysis of Randomised Controlled Trials

Purpose: Surgical options for long head of the biceps tendon (LHBT) lesions include tenotomy and tenodesis. This study aims to determine the optimal surgical strategy for LHBT lesions with updated evidence from randomised controlled trials (RCTs). Methods: Literature was retrieved from PubMed, Cochrane Library, Embase and Web of Science on 12 January 2022. Randomised controlled trials (RCTs) comparing the clinical outcomes of tenotomy and tenodesis were pooled in the meta-analyses. Results: Ten RCTs with 787 cases met the inclusion criteria, and were included in the meta-analysis. Constant scores (MD, −1.24; p = 0.001), improvement of Constant scores (MD, −1.54; p = 0.04), Simple Shoulder Test (SST) scores (MD, −0.73; p = 0.03) and improvement of SST (p < 0.05) were significantly better in patients with tenodesis. Tenotomy was associated with higher rates of Popeye deformity (OR, 3.34; p < 0.001) and cramping pain (OR, 3.36; p = 0.008]. No significant differences were noticed between tenotomy and tenodesis regarding pain (p = 0.59), American Shoulder and Elbow Surgeons (ASES) score (p = 0.42) and its improvement (p = 0.91), elbow flexion strength (p = 0.38), forearm supination strength (p = 0.68) and range of motion of shoulder external rotation (p = 0.62). Subgroup analyses showed higher Constant scores in all tenodesis types and significantly larger improvement of Constant scores regarding intracuff tenodesis (MD, −5.87; p = 0.001). Conclusions: According to the analyses of RCTs, tenodesis better improves shoulder function in terms of Constant scores and SST scores, and reduces the risk of Popeye deformity and cramping bicipital pain. Intracuff tenodesis might offer the best shoulder function as measured with Constant scores. However, tenotomy and tenodesis provide similar satisfactory results for pain relief, ASES score, biceps strength and shoulder range of motion.
Evidence of Dirac fermions in multilayer silicene

Multilayer silicene, the silicon analogue of multilayer graphene, grown on silver (111) surfaces, possesses a honeycomb (√3 × √3)R30° reconstruction, observed by scanning tunnelling microscopy at room temperature, past the initial formation of the dominant, 3×3 reconstructed, silicene monolayer. For a few layers silicene film we measure by synchrotron radiation photoelectron spectroscopy, a cone-like dispersion at the Brillouin zone centre due to band folding. π* and π states meet at ∼0.25 eV below the Fermi level, providing clear evidence of the presence of gapless Dirac fermions.
Association between coronary artery disease and incident cancer risk: a systematic review and meta-analysis of cohort studies

Objective Coronary artery disease (CAD) and cancer are the two leading causes of death worldwide. Evidence suggests the existence of shared mechanisms for these two diseases. We aimed to conduct a systematic review and meta-analysis to investigateassociation between CAD and incident cancer risk. Methods We searched Cochrane, PubMed, and Embase from inception until October 20, 2021, without language restrictions. Observational cohort studies were used to investigate the association between CAD and incident cancer risk. Using random-effects models, the odds ratio (OR) and 95% confidence interval (CI) were calculated. We utilized subgroup and sensitivity analyses to determine the potential sources of heterogeneity and explore the association between CAD and specific cancers. This study was conducted under a pre-established, registered protocol on PROSPERO (CRD42022302507). Results We initially examined 8,533 articles, and included 14 cohort studies in our review, 11 of which were eligible for meta-analysis. Patients with CAD had significantly higher odds of cancer risk than those without CAD (OR = 1.15, 95% CI = [1.08–1.22], I2 = 66%). Subgroup analysis revealed that the incident cancer risk was significantly higher in both sexes and patients with CAD with or without myocardial infarction. Sensitivity analysis revealed that the risk remained higher in patients with CAD even after >1 year of follow-up (OR = 1.23, 95% CI = [1.08–1.39], I2 = 76%). Regarding the specific outcome, the incident risk for colorectal and lung cancers was significantly higher (OR = 1.06, 95% CI = [1.03–1.10], I2 = 10%, and OR = 1.36, 95% CI = [1.15–1.60], I2 = 90%, respectively) and that for breast cancer was lower (OR = 0.86, 95% CI = [0.77–0.97], I2 = 57%) in patients with CAD than in those without CAD. Conclusion CAD may be associated with incident cancer risk, particularly for lung and colorectal cancers, in men and women as well as patients with or without myocardial infarction. Early detection of new-onset cancer and detailed cancer surveillance programs should be implemented in patients with CAD to reduce cancer-related morbidity and mortality.
Nonlinear optimization approaches to H2-norm based LPV modelling and control

To be able to analyze certain classes of non-linear systems, it is necessary to try to represent them as linear parameter varying systems or even as linear fractional representations. For linear parameter varying systems and linear fractional representations of systems there exists many advanced analysis methods such as IQC-analysis and μ-analysis. This means that an important intermediate step in all this is to generate a linear parameter varying model that describes these non-linear system sufficiently well. The first contribution in this thesis is a novel method that tries, through nonlinear programming and a quasi-Newton framework, to generate a linear parameter varying model given linearized state space models. The idea behind the method is to preserve the input-output relations of the given linearized systems and, in an H2-measure, find the best one. To handle uncertainties in data an extension of the proposed method is presented. It is shown how the computationally hard robust optimization approach to the uncertain case can be approximated using a problem specific regularization. The second contribution in this thesis is a method for synthesizing output-feedback H2 controllers of arbitrary order. This method also uses non-linear programming and a quasi-Newton framework to achieve this. One great benefit with this method is that it also possible to impose structure in the controller. Both of the methods described above tries to solve non-linear and non-convex problems, which means that the problem of finding a good initial estimate is an important problem. For both methods an initialization procedure is proposed to try to find an initial estimate. The methods are evaluated on several examples and show promising results. A contributing factor is that significant effort has been spent on utilizing the structure of the optimization problems to make the methods efficient.
Optimal filtering for systems with unknown inputs

An optimal filtering formula is derived for linear time-varying discrete systems with unknown inputs. By making use of the well-known innovations filtering technique, the derivation is an extension of a new observer design method for time-invariant deterministic systems with unknown inputs. The systems under consideration have the most general form. The derived optimal filter has a similar form to the standard Kalman filter with some modified covariance and gain matrices.
A Unit Compound Structure Design: Poisson’s Ratio Is Autonomously Adjustable from Negative to Positive

The shape memory polymer (SMP) is a new type of smart material that can produce a shape memory effect through the stimulation of the external environment. In this article, the viscoelastic constitutive theory of the shape memory polymer and the mechanism of the bidirectional memory effect of the shape memory polymer are described. A chiral poly cellular circular concave auxetic structure based on a shape memory polymer made of epoxy resin is designed. Two structural parameters, α and β, are defined, and the change rule of Poisson’s ratio under different structural parameters is verified by ABAQUS. Then, two elastic scaffolds are designed to assist a new type of cellular structure made of a shape memory polymer to autonomously adjust bidirectional memory under the stimulation of the external temperature, and two processes of bidirectional memory are simulated using ABAQUS. Finally, when a shape memory polymer structure implements the bidirectional deformation programming process, a conclusion is drawn that changing the ratio β of oblique ligament and ring radius has a better effect than changing the angle α of oblique ligament and horizontal in achieving the autonomously adjustable bidirectional memory effect of the composite structure. In summary, through the combination of the new cell and the bidirectional deformation principle, the autonomous bidirectional deformation of the new cell is achieved. The research can be used in reconfigurable structures, tuning symmetry, and chirality. The adjusted Poisson’s ratio achieved by the stimulation of the external environment can be used in active acoustic metamaterials, deployable devices, and biomedical devices. Meanwhile, this work provides a very meaningful reference for the potential application value of metamaterials.
THE DARK HALO OF THE POLAR-RING GALAXY NGC-4650A

By analyzing the rotation of its central disk and outer polar ring, limits are placed on the flattening of the unseen halo of the galaxy NGC 4650A. A model consisting of a bulge, disk, axisymmetric dark halo, and massive polar ring is fitted to both rotation curves, and it is shown that the observations are consistent with a range of halo flattening, from spherical to as flat as an E8 elliptical galaxy. The best fits are obtained for dark halo flattenings in the range E3-E7. The substantial mass of the ring is probably responsible for a characteristic wiggle in the polar rotation curve; fits to this feature suggest that there may be considerably more mass in the ring than has been reported from neutral hydrogen measurements. 30 refs.
Investigation of trap-assisted tunneling current in InAs/(GaIn)Sb superlattice long-wavelength photodiodes

Trap centers with an energy level positioned 1/3 of the band gap below the effective conduction band edge are observed in the electroluminescence spectra of InAs/(GaIn)Sb superlattice photodiodes with a cutoff wavelength of 11 μm. The trap centers are recognized by simulating the low-temperature current–voltage characteristics of the diodes. Excellent quantitative agreement on both, the I–V characteristic and the differential resistance between the experimental data and the theoretical prediction is achieved. The quantitative simulation of the I–V characteristics shows, that the 77 K performance of InAs/(GaIn)Sb photodiodes is dominated by generation-recombination processes even at long wavelengths. Above 50 K, tunneling currents are not of importance.