Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Micro- and nanoplastics are considered one of the top pollutants that threaten the environment, aquatic life, and mammalian (including human) health. Unfortunately, the development of uncomplicated but reliable analytical methods that are sensitive to individual microplastic particles, with sizes smaller than 1 μm, remains incomplete. Here, we demonstrate the detection and identification of (single) micro- and nanoplastics by using surface-enhanced Raman spectroscopy (SERS) with Klarite substrates. Klarite is an exceptional SERS substrate; it is shaped as a dense grid of inverted pyramidal cavities made of gold. Numerical simulations demonstrate that these cavities (or pits) strongly focus incident light into intense hotspots. We show that Klarite has the potential to facilitate the detection and identification of synthesized and atmospheric/aquatic microplastic (single) particles, with sizes down to 360 nm. We find enhancement factors of up to 2 orders of magnitude for polystyrene analytes. In addition, we detect and identify microplastics with sizes down to 450 nm on Klarite, with samples extracted from ambient, airborne particles. Moreover, we demonstrate Raman mapping as a fast detection technique for submicron microplastic particles. The results show that SERS with Klarite is a facile technique that has the potential to detect and systematically measure nanoplastics in the environment. This research is an important step toward detecting nanoscale plastic particles that may cause toxic effects to mammalian and aquatic life when present in high concentrations.

Summary Recently, the environmental impacts of microplastics have received extensive attention owing to their accumulation in the environment. However, developing efficient technology for the control and purification of microplastics is still a big challenge. Herein, we investigated the photocatalytic degradation of typical microplastics such as polystyrene (PS) microspheres and polyethylene (PE) over TiO 2 nanoparticle films under UV light irradiation. TiO 2 nanoparticle film made with Triton X-100 showed complete mineralization (98.40%) of 400-nm PS in 12 h, while degradation for varying sizes of PS was also studied. PE degradation experiment presented a high photodegradation rate after 36 h. CO 2 was found as the main end product. The degradation mechanism and intermediates were studied by in situ DRIFTS and HPPI-TOFMS, showing the generation of hydroxyl, carbonyl, and carbon-hydrogen groups during the photodegradation of PS. This study provides a green and cost-efficient strategy for the control of microplastics contamination in the environment.

The maize (Zea mays L.) CRINKLY4 (CR4) gene encodes a serine/threonine receptor-like kinase that controls an array of developmental processes in the plant and endosperm. The Arabidopsis thaliana (L.) Heynh. genome encodes an ortholog of CR4, ACR4, and four CRINKLY4-RELATED (CRR) proteins: AtCRR1, AtCRR2, AtCRR3 and AtCRK1. The available genome sequence of rice (Oryza sativa L.) encodes a CR4 ortholog, OsCR4, and four CRR proteins: OsCRR1, OsCRR2, OsCRR3 and OsCRR4, not necessarily orthologous to the Arabidopsis CRRs. A phylogenetic study showed that AtCRR1 and AtCRR2 form a clade closest to the CR4 group while all the other CRRs form a separate cluster. The five Arabidopsis genes are differentially expressed in various tissues. A construct formed by fusion of the ACR4 promoter and the GUS reporter, ACR4::GUS, is expressed primarily in developing tissues of the shoot. The ACR4 cytoplasmic domain functions in vitro as a serine/threonine kinase, while the AtCRR1 and AtCRR2 kinases are not active. The ability of ACR4 to phosphorylate AtCRR2 suggests that they might function in the same signal transduction pathway. T-DNA insertions were obtained in ACR4, AtCRR1, AtCRR2, AtCRR3 and AtCRK1. Mutations in acr4 show a phenotype restricted to the integuments and seed coat, suggesting that Arabidopsis might contain a redundant function that is lacking in maize. The lack of obvious mutant phenotypes in the crr mutants indicates they are not required for the hypothetical redundant function.

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