Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysis

Background: Pronuclear assessment appears to have the ability to distinguish good and bad embryos in the zygote stage,but paradoxical results were obtained in clinical studies.This situation might be caused by the robust qualitative detection of the development of dynamic pronuclei. Here,we aim to establish a quantitative pronuclear measurement method by applying expert experience deep learning from large annotated datasets. Methods: Convinced handle-annotated 2PN images(13419) were used for deep learning then corresponded errors were recorded through handle check for subsequent parameters adjusting. We used 790 embryos with 52479 PN images from 155 patients for analysis the area of pronuclei and the preimplantation genetic test results.Establishment of the exponential fitting equation and the key coefficient β1 was extracted from the model for quantitative analysis for pronuclear(PN) annotation and automatic recognition. Findings: Based on the female original PN coefficient β1,the chromosome normal rate in the blastocyst with biggest PN area is much higher than that of the blastocyst with smallest PN area(58.06% vs.45.16%, OR=1.68[1.07-2.64];P=0.031).After adjusting coefficient β1 by the first three frames which high variance of outlier PN areas was removed, coefficient β1 at 12 hours and at 14 hours post-insemination,similar but stronger evidence was obtained. All these discrepancies resulted from the female propositus in the PGT(SR) subgroup and smaller chromosomal errors. Conclusion(s): The results suggest that detailed analysis of the images of embryos could improve our understanding of developmental biology. Funding: None

On exponential fitting of finite difference methods for heat equations

In this article, a new kind of finite difference scheme that is exponentially fitted, inspired from Fourier analysis, for a fourth space derivative was developed for solving diffusion problems. Dispersion relation and local truncation error of the method were discussed. Stability analysis of the method revealed that it is conditionally stable. Compared to the corresponding fourth order classical scheme in the literature, the proposed scheme is efficient and accurate. Mathematics Subject Classification (2020): 65M06, 65N06. Keywords: Exponential fitting, Finite difference, Local truncation error, Heat equations.

Study of Rheological Behavior of Olive Oil Used as Biodegradable Lubricant

In this article, based on experimental data, we obtained three dependence relations of dynamic viscosity versus temperature by polynomial and exponential fitting. The correlation coefficients have values close to unity which proves that the obtained relations accurately describe the non-Newtonian behavior of olive oil.

Investigation on Shear Wave Velocity and Triaxial Mechanical Performance of Tailings Core from Tailings Dam

The physical and mechanical parameters of tailings are important to study the stability of tailings dams (TDs). In this study, a series of laboratory experiments (shear wave velocity, triaxial compressive, and peak strain strength testing) were conducted to obtain the mechanical properties of tailings from TD. The results showed the following. (1) The linear function fitting could characterize the quantitative relationship between shear wave velocity and hole depth. (2) The corresponding static pressure coefficient increased as the confining pressure increased. The exponential fitting could characterize the quantitative function relation between the static pressure coefficient and the confining pressure. (3) The cohesion and internal friction angle of the tailings sample were 20 kPa and 41°, respectively, and the logarithmic fitting could better characterize the quantitative relation between shear peak strength and confining pressure. The results of this study can provide important references for further research on the stability of TD.

Bioenergetic Alterations of Metabolic Redox Coenzymes as NADH, FAD and FMN by Means of Fluorescence Lifetime Imaging Techniques

Metabolic FLIM (fluorescence lifetime imaging) is used to image bioenergetic status in cells and tissue. Whereas an attribution of the fluorescence lifetime of coenzymes as an indicator for cell metabolism is mainly accepted, it is debated whether this is valid for the redox state of cells. In this regard, an innovative algorithm using the lifetime characteristics of nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) to calculate the fluorescence lifetime induced redox ratio (FLIRR) has been reported so far. We extended the FLIRR approach and present new results, which includes FLIM data of the various enzymes, such as NAD(P)H, FAD, as well as flavin mononucleotide (FMN). Our algorithm uses a two-exponential fitting procedure for the NAD(P)H autofluorescence and a three-exponential fit of the flavin signal. By extending the FLIRR approach, we introduced FLIRR1 as protein-bound NAD(P)H related to protein-bound FAD, FLIRR2 as protein-bound NAD(P)H related to free (unbound) FAD and FLIRR3 as protein-bound NAD(P)H related to protein-bound FMN. We compared the significance of extended FLIRR to the metabolic index, defined as the ratio of protein-bound NAD(P)H to free NAD(P)H. The statistically significant difference for tumor and normal cells was found to be highest for FLIRR1.

Nanosecond-order long–short fluorescence lifetime switchable encryption with enlarged coding capacity

The turn-off fluorescent photoswitches for information encryption are constantly being developed. However, there are no reports about time-switchable (fluorescence lifetime-switchable) encryption to overcome the limitations of tunable encoding numbers in spectrally and temporally encoded libraries. Based on the double-exponential fitting of fluorescence lifetime, we propose, a fatigue-free and highly flexible switch between the amplitude-weighted average fluorescence lifetime (τm) and the intensity-weighted average fluorescence lifetime (τi), which will realize the supermultiplexed fluorescence lifetime switchable encryption. The potentially enormous library of different fluorescent lifetime combinations would facilitate the development of information security.

A Single-Phase Modelling for the Oxygen Uptake Rate in Excess Post-Exercise Oxygen Consumption

A two-parameters [a rate constant k and a factor f (0≤f < 1)] modelling, describing satisfactorily the post-exercise oxygen uptake rate (VO2) as a function of the recovery time (t), is presented. f controls the rate equation dVO2/dt, particularly at t = 0 where (dVO2/dt)t=0 ∝ −k(1−f), a less abrupt decay than (dVO2/dt)t=0 ∝ −k expected from an exponential. Fitting the model to a set of experimental VO2 vs t data after a 3MT it was found a set of values with f close to 0 and another with f > 1/2, with a narrow distribution of values for the half-recovery time τ1/2=(1/k)ln[(2−f)/(1−f)] (〈τ1/2〉=0.641 min, σ = 0.062 min), very similar to that (T) found by fitting a model based on a logit transform (〈T〉 = 0.672 min, σ = 0.081 min). The parameter f is a reliable index of the initial acceleration of the oxygen uptake rate recovery (and likely of the heart rate recovery) and, together with the half-recovery time τ1/2, may be a useful method in characterizing and monitoring performs and exercise forms, very important in the physiology area.

Spotting Frozen Curd in PDO Buffalo Mozzarella Cheese Through Insights on Its Supramolecular Structure Acquired by 1H TD-NMR Relaxation Experiments

The addition of frozen curd (FC) during the production process of “Mozzarella di Bufala Campana”, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet available.

Method for detecting vertical offset of coupling coil using signal coil based on magnetic coupling resonance wireless power transfer system

To improve the reliability in wireless power transfer system, communication is widely used. An independent coil as the signal coil for data transfer has the advantage of decoupling power coupling mechanism and signal coil when designing the system. Besides, in some application, vertical offset of the power coil may impact the stability of the system, position detection of the receiver side power coil is needed. Method of using the signal coil for vertical offset detection is adopted in this paper, simulation result verified the feasibility. The vertical offset of the power coil can be estimated by an exponential function based on the system parameter and the current of the primary side power coil. The exponential fitting function used in the paper can be used as a reference when designing the detection.