An integrated chromatin accessibility and transcriptome landscape of human pre-implantation embryos

Early human embryonic development involves extensive changes in chromatin structure and transcriptional activity. Here the authors present LiCAT-seq, a method enabling simultaneous profiling of chromatin accessibility and gene expression with ultra-low input of cells and map chromatin accessibility and transcriptome landscapes for human pre-implantation embryos

Fast Training Logistic Regression via Adaptive Sampling

Logistic regression has been widely used in artificial intelligence and machine learning due to its deep theoretical basis and good practical performance. Its training process aims to solve a large-scale optimization problem characterized by a likelihood function, where the gradient descent approach is the most commonly used. However, when the data size is large, it is very time-consuming because it computes the gradient using all the training data in every iteration. Though this difficulty can be solved by random sampling, the appropriate sampled examples size is difficult to be predetermined and the obtained could be not robust. To overcome this deficiency, we propose a novel algorithm for fast training logistic regression via adaptive sampling. The proposed method decomposes the problem of gradient estimation into several subproblems according to its dimension; then, each subproblem is solved independently by adaptive sampling. Each element of the gradient estimation is obtained by successively sampling a fixed volume training example multiple times until it satisfies its stopping criteria. The final estimation is combined with the results of all the subproblems. It is proved that the obtained gradient estimation is a robust estimation, and it could keep the objective function value decreasing in the iterative calculation. Compared with the representative algorithms using random sampling, the experimental results show that this algorithm obtains comparable classification performance with much less training time

Exploring the efficacy and beneficial population of preimplantation genetic testing for aneuploidy start from the oocyte retrieval cycle: a real-world study

Abstract Background Preimplantation genetic testing for aneuploidy (PGT-A) is widely used as an embryo selection technique in in vitro fertilization (IVF), but its effectiveness and potential beneficiary populations are unclear. Methods This retrospective cohort study included patients who underwent their first oocyte retrieval cycles at CITIC-Xiangya between January 2016 and November 2019, and the associated fresh and thawed embryo transfer cycles up to November 30, 2020. PGT-A (PGT-A group) and intracytoplasmic sperm injection (ICSI)/IVF (non-PGT-A group) cycles were included. The numbers of oocytes and embryos obtained were unrestricted. In total, 60,580 patients were enrolled, and baseline data were matched between groups using 1:3 propensity score matching. Sensitivity analyses, including propensity score stratification and traditional multivariate logistic regression, were performed on the original unmatched cohort to check the robustness of the overall results. Analyses were stratified by age, body mass index, ovarian reserve/responsiveness, and potential indications to explore benefits in subgroups. The primary outcome was cumulative live birth rate (CLBR). The other outcomes included live birth rate (LBR), pregnancy loss rate, clinical pregnancy rate, pregnancy complications, low birth weight rate, and neonatal malformation rate. Results In total, 4195 PGT-A users were matched with 10,140 non-PGT-A users. A significant reduction in CLBR was observed in women using PGT-A (27.5% vs. 31.1%; odds ratio (OR) = 0.84, 95% confidence interval (CI) 0.78–0.91; P < 0.001). However, women using PGT-A had higher first-transfer pregnancy (63.9% vs. 46.9%; OR = 2.01, 95% CI 1.81–2.23; P < 0.001) and LBR (52.6% vs. 34.2%, OR = 2.13, 95% CI 1.92–2.36; P < 0.001) rates and lower rates of early miscarriage (12.8% vs. 20.2%; OR = 0.58, 95% CI 0.48–0.70; P < 0.001), preterm birth (8.6% vs 17.3%; P < 0.001), and low birth weight (4.9% vs. 19.3%; P < 0.001). Moreover, subgroup analyses revealed that women aged ≥ 38 years, diagnosed with recurrent pregnancy loss or intrauterine adhesions benefited from PGT-A, with a significant increase in first-transfer LBR without a decrease in CLBR. Conclusion PGT-A does not increase and decrease CLBR per oocyte retrieval cycle; nonetheless, it is effective in infertile populations with specific indications. PGT-A reduces complications associated with multiple gestations

The Relationship between Cell Number, Division Behavior and Developmental Potential of Cleavage Stage Human Embryos: A Time-Lapse Study

<div><p>Day 3 cleavage embryo transfer is routine in many assisted reproductive technology centers today. Embryos are usually selected according to cell number, cell symmetry and fragmentation for transfer. Many studies have showed the relationship between cell number and embryo developmental potential. However, there is limited understanding of embryo division behavior and their association with embryo cell number and developmental potential. A retrospective and observational study was conducted to investigate how different division behaviors affect cell number and developmental potential of day 3 embryos by time-lapse imaging. Based on cell number at day 3, the embryos (from 104 IVF/intracytoplasmic sperm injection (ICSI) treatment cycles, n = 799) were classified as follows: less than 5 cells (< 5C; n = 111); 5–6 cells (5–6C; n = 97); 7–8 cells (7–8C; n = 442), 9–10 cells (9–10C; n = 107) and more than 10 cells (>10C; n = 42). Division behavior, morphokinetic parameters and blastocyst formation rate were analyzed in 5 groups of day 3 embryos with different cell numbers. In <5C and 5–6C embryos, fragmentation (FR; 62.2% and 30.9%, respectively) was the main cause for low cell number. The majority of 7–8C embryos exhibited obvious normal behaviors (NB; 85.7%) during development. However, the incidence of DC in 9–10C and >10C embryos increased compared to 7–8C embryos (45.8%, 33.3% vs. 11.1%, respectively). In ≥5C embryos, FR and DC significantly reduced developmental potential, whereas <5C embryos showed little potential irrespective of division behaviors. In NB embryos, the blastocyst formation rate increased with cell number from 7.4% (<5C) to 89.3% (>10C). In NB embryos, the cell cycle elongation or shortening was the main cause for abnormally low or high cell number, respectively. After excluding embryos with abnormal division behaviors, the developmental potential, implantation rate and live birth rate of day 3 embryos increased with cell number.</p></div

Implantation rate and live birth rate in NB embryos with different cell number.

<p>Implantation rate and live birth rate in NB embryos with different cell number.</p

Different morphokinetics parameters in low- and high cell number NB embryos.

<p>(A) The first three mitosis cycles of NB embryos from insemination to 8 cells. cc1: duration of first cell cycle or mitosis, from completion of 2 cells to insemination; Ecc2: duration of second embryo cell cycle, from 2 to 4 cells; Ecc3: duration of third embryo cell cycle, from 4 to 8 cells. (B) Different morphokinetics parameters in low- and high cell number NB embryos. <5 C: NB embryos in <5 C embryos; 5− 8C: NB embryos in 5–8C embryos; > 8C: NB embryos in >8 C embryos. **<i>P</i> < 0.01.</p

Detection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method

Microplastics are sub-millimeter-sized fragments of plastics, which have been found in environments to a great extent. They are relatively new pollutants that are difficult to be degraded. They not only cause irreversible adverse effects on microorganisms, animals, and plants but also enter the human body through the food chain and affect human health. However, due to their small size, variety, and differences in physical and chemical properties of microplastics, traditional detection and identification still face challenges. This work provides a method for detecting and classifying microplastics in liquids using a liquid–solid triboelectric nanogenerator (LS-TENG) in combination with a deep learning model. The experiment showed that the type and content of microplastics in the liquid had a great effect on the contact electrification between the liquid and the perfluoroethylene-propylene copolymer. After adding polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polystyrene microplastics to the liquids, it was found that the type and content of different microplastics have a significant impact on the output voltage signal of the LS-TENG sensor. When the mass fraction of microplastics ranged from 0.025 to 0.25 wt %, the voltage output of the LS-TENG sensor had a linear relationship with the mass fraction of microplastics. Therefore, a method for quantitatively detecting the content of microplastics using the LS-TENG sensor has been established. Based on the LS-TENG output voltage signal, a convolutional neural network deep learning model was used to identify different types of labels, and high recognition accuracy was achieved. These are of great significance for expanding the application prospect of LS-TENG and realizing the detection of microplastics in liquids

Cell number distribution for all embryos on day 3.

<p>Cell number distribution for all embryos on day 3.</p