Single-Image-Based Deep Learning for Precise Atomic Defect Identification.

Defect engineering is widely used to impart the desired functionalities on materials. Despite the widespread application of atomic-resolution scanning transmission electron microscopy (STEM), traditional methods for defect analysis are highly sensitive to random noise and human bias. While deep learning (DL) presents a viable alternative, it requires extensive amounts of training data with labeled ground truth. Herein, employing cycle generative adversarial networks (CycleGAN) and U-Nets, we propose a method based on a single experimental STEM image to tackle high annotation costs and image noise for defect detection. Not only atomic defects but also oxygen dopants in monolayer MoS2 are visualized. The method can be readily extended to other two-dimensional systems, as the training is based on unit-cell-level images. Therefore, our results outline novel ways to train the model with minimal data sets, offering great opportunities to fully exploit the power of DL in the materials science community.

Identification of two novel heterozygous variants of SMC3 with Cornelia de Lange syndrome.

BACKGROUND: Cornelia de Lange syndrome (CdLS) is a multisystem genetic disorder, and cases caused by variants in the structural maintenance of chromosomes protein 3 (SMC3) gene are uncommon. Here, we report two cases of CdLS associated with novel pathogenic variants in SMC3 from two Chinese families. METHODS: Clinical presentations of two patients with CdLS were evaluated, and specimens from the patients and other family members were collected for Trio-based whole-exome sequencing. Pyrosequencing, chip-based digital PCR, minigene splicing assay, and in silico analysis were carried out to elucidate the impact of novel variants. RESULTS: Novel heterozygous variants in SMC3 were identified in each proband. One harbored a novel splicing and mosaic variant (c.2535+1G>A) in SMC3. The mutated allele G>A conversion was approximately 23.1% by digital PCR, which indicated that 46.2% of peripheral blood cells had this variant. Additionally, in vitro minigene splicing analysis validated that the c.2535+1G>A variant led to an exon skipping in messenger RNA splicing. The other carried a heterozygous variant (c.435C>A), which was predicted to be pathogenic as well as significantly altered in local electrical potential. The former showed multiple abnormalities and marked clinical severity, and the latter mainly exhibited a speech developmental disorder and slightly facial anomalies. CONCLUSION: Both patients were clinically diagnosed with Cornelia de Lange syndrome 3 (CdLS3). The newly identified SMC3 gene variants can expand the understanding of CdLS3 and provide reliable evidence for genetic counseling to the affected family.

Inhibiting Polyamide Intrusion of Thin Film Composite Membranes: Strategies and Environmental Implications.

Thin film composite polyamide (TFC) nanofiltration (NF) membranes represent extensive applications at the water-energy-environment nexus, which motivates unremitting efforts to explore membranes with higher performance. Intrusion of polyamide into substrate pores greatly restricts the overall membrane permeance because of the excessive hydraulic resistance, while the effective inhibition of intrusion remains technically challenging. Herein, we propose a synergetic regulation strategy of pore size and surface chemical composition of the substrate to optimize selective layer structure, achieving the inhibition of polyamide intrusion effective for the membrane separation performance enhancement. Although reducing the pore size of the substrate prevented polyamide intrusion at the intrapore, the membrane permeance was adversely affected due to the exacerbated "funnel effect". Optimizing the polyamide structure via surface chemical modification of the substrate, where reactive amino sites were in situ introduced by the ammonolysis of polyethersulfone substrate, allowed for maximum membrane permeance without reducing the substrate pore size. The optimal membrane exhibited excellent water permeance, ion selectivity, and emerging contaminants removal capability. The accurate optimization of selective layer is anticipated to provide a new avenue for the state-of-the-art membrane fabrication, which opens opportunities for promoting more efficient membrane-based water treatment applications.

Microfluidic Devices for Monitoring the Root Morphology of Arabidopsis Thaliana in situ.

Plant roots play critical roles in absorbing nutrients for the growth and development of plants as well as adapting different environments. Currently, there is no satisfactory way to track dynamic information when studying roots at the high temporal and spatial resolution. Herein, a simple microfluidic device with crossed microchannels was utilized for a microscopic investigation of Arabidopsis thaliana roots in situ. Our experimental results showed that the microfluidic system combined with a microscope could be conveniently utilized for the quantification of primary roots and root hairs with a change of micrometers within a time of minutes. Using the same approach, the influences of high salinity stress could also be investigated on different parts of roots, including the root cap, meristematic zone, elongation zone, mature zone, and root hairs. More importantly, the growth of roots and root hairs could be quantified and compared in a solution of abscisic acid and indole-3-acetic acid, respectively. Our study suggested that the microfluidic system could become a powerful tool for the quantitative investigation of Arabidopsis thaliana roots.