Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study.

Background: Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement.

Pituitary stalk interruption syndrome: a case report and literature review.

Background: Pituitary stalk interruption syndrome is a rare congenital anomaly of the pituitary gland characterized by growth hormones deficiency (with or without other pituitary hormone deficiencies) along with radiological features of a thin or interrupted pituitary stalk, an ectopic or absent posterior pituitary, or a hypoplastic or absent anterior pituitary.

Case Presentation: A 10-year-old baby boy came with short stature. The laboratory investigations were done and showed low growth hormones and low thyroid-stimulating hormone.

Pressure-Dependent CO Electroreduction to Methane over Asymmetric Cu-N Single-Atom Sites.

Single-atom catalysts (SACs) with unitary active sites hold great promise for realizing high selectivity toward a single product in the CO electroreduction reaction (CORR). However, achieving high Faradaic efficiency (FE) of multielectron products like methane on SACs is still challenging. Herein, we report a pressure-regulating strategy that achieves 83.

CP-Net: Instance-aware part segmentation network for biological cell parsing.

Instance segmentation of biological cells is important in medical image analysis for identifying and segmenting individual cells, and quantitative measurement of subcellular structures requires further cell-level subcellular part segmentation. Subcellular structure measurements are critical for cell phenotyping and quality analysis. For these purposes, instance-aware part segmentation network is first introduced to distinguish individual cells and segment subcellular structures for each detected cell.

EEG-based emotion recognition systems; comprehensive study.

Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features.