Identification of Genomic Alterations in Sporadic Pancreatic Neurogenic Tumors.

Pancreatic neurogenic tumors, including schwannoma and neurofibroma, are rare, and their genetic aberrances have not been defined. The present study aimed at investigating the genomic alterations of pancreatic schwannoma and neurofibroma. Two patients with pancreatic schwannoma and 1 patient with neurofibroma, who underwent surgical resection at the First Affiliated Hospital, Sun Yat-sen University between June 2016 and April 2019, were recruited into the study. Their tumor tissues were analyzed by exome sequencing and genome sequencing. Exome sequencing revealed a MUTYH likely pathogenic germline variant in 1 schwannoma with somatic NF2del and NOTCH1 amplification. Pathway enrichment analysis on the other schwannoma case showed that the main abnormal function involved DNA damage repair, mitosis, and cell cycle. In addition, genome sequencing showed the inversion (INV) variant of SPIRE gene and multiple mitochondrial INV variants in both schwannoma cases. Furthermore, exome sequencing revealed NF1del, single nucleotide variation, TP53, and ERBB3 amplification in neurofibroma, whereas genomic duplication/deletion variants and mitochondrial abnormalities were much less than that in schwannoma. In conclusion, variants in NF1 and NF2 genes, amplification of key driver genes, and somatic and mitochondrial INV variants may play important roles in the development of pancreatic schwannoma and neurofibroma.

What is the transfer mechanism of photogenerated carriers for the nanocomposite photocatalyst Ag3PO4/g-C3N4, band-band transfer or a direct Z-scheme?

The separation mechanisms of photoexcited carriers for composite photocatalysts are a hot point in the photocatalytic field. In this paper, the Ag3PO4/g-C3N4 nanocomposites with different main parts (Ag3PO4 or g-C3N4) were synthesized using a facile in situ precipitation method. The photocatalysts were characterized by X-ray powder diffraction, UV-vis diffuse reflection spectroscopy, transmission electron microscopy and Brunauer-Emmett-Teller methods. The photocatalytic performance was evaluated by the degradation of methylene blue under visible light irradiation. When the main part of the Ag3PO4/g-C3N4 photocatalyst is Ag3PO4, the transfer mechanism of photogenerated electron-hole takes generic band-band transfer, and the photocatalytic activity is decreased. However, when the primary part of the Ag3PO4/g-C3N4 photocatalyst is g-C3N4, the migration of photogenerated electron-hole exhibits a typical Z-scheme mechanism, and the photocatalytic activity is increased greatly. The separation mechanisms of photogenerated carriers were investigated by the electron spin resonance technology, the photoluminescence technique and the determination of reactive species in the photocatalytic reactions. It is hoped that this work could render guided information for design and application of Z-scheme photocatalysts with excellent photocatalytic performance.

Optimization of the single-phased white phosphor of Li2SrSiO4: Eu2+, Ce3+ for light-emitting diodes by using the combinatorial approach assisted with the Taguchi method.

The best performance of the phosphor Li(2)SrSiO(4): Eu(2+), Ce(3+) in terms of luminescence efficiency (LE), color rendering index (CRI) and color temperature (Tc) for light-emitting diode application was optimized with combinatorial approach. The combinatorial libraries were synthesized with solution-based method and the scale-up samples were synthesized with conventional solid-reaction method. Crystal structure was investigated by using the X-ray diffraction spectrometer. The emission spectra of each sample in combinatorial libraries were measured in situ by using a fiber optic spectrometer. Fluorescence spectrometers were used to record excitation and emission spectra of bulk samples. White light generation was tuned up by tailoring Eu(2+) and Ce(3+) concentrations in the single-phased host of Li(2)SrSiO(4) under near-ultraviolet excitation, but it exhibited low efficiency of luminescence and poor color rendering index. The effects of each level of the Eu(2+) and Ce(3+) concentrations on LE, CRI, and Tc were evaluated with the Taguchi method. The optimum levels of the interaction pairs between Eu(2+) and Ce(3+) concentration on LE, CRI, and Tc were [2, 1] (0.006 M, 0.003 M), [1, 2] (0.003 M, 0.006 M), and [3, 1] (0.009 M, 0.00 3M), respectively. The thermal stability of luminescence, the external quantum efficiency (QE), luminance, chromaticity coordinates, correlated color temperature, color purity including the composition ratio of RGB in white light, and color rendering index of the white light emission of phosphor were evaluated comprehensively from a bulk sample.

[Implementation and application of a digital cytopathological lung cancer diagnosing system].

OBJECTIVE: To evaluate the efficacy of the digital cytopathological lung cancer diagnosing system (DCLCDS) utilizing the latest computer technologies (including reinforcement learning, image segmentation and classifier) and the cytopathological knowledge on lung cancer cells. METHODS: Separate the overlapped lung cancer cells in a slice image applying the improved deBoor-Cox B-Spline algorithm; Segment cell regions in a slice image using an image segmentation algorithm based on reinforcement learning; Ensemble different classifiers, including Decision Tree classifier, Support Vector Machine (SVM) classifier and Bayesian classifier, to achieve an accurate result of cytopathological lung cancer diagnosis. RESULTS: The accurate diagnosis rate for lung cancer identification of 224 images of small lung lesions aspiration biopsy from 120 cases randomly selected was 92.3%. The accurate diagnosis rate for type classification of lung cancer was 82.5%. The identification rate for abnormal nuclear cells was 71.6%. CONCLUSIONS: The DCLCDS achieves a high accuracy on cytopathological lung cancer diagnosis by solving some major problems on the cytology smears, including cell overlapping, uneven coloration and impurity. It provides a relatively objective, standard tool on cytopathological lung cancer diagnosis. It has good efficacy on early diagnosis of lung cancer.

TiO2/beads as a photocatalyst for the degradation of X3B azo dye.

The feasibility of photocatalytic degradation of X3B azo dye by TiO2/beads photocatalyst was studied. The effects of parameters such as the amount of TiO2/beads, airflow, as well as the concentrations of H2O2, Fe3+, Mg2+ and Na+ on the photocatalytic degradation of X3B azo dye were also studied. The results showed that 25 mg/dm3 X3B azo dye can be photocatalytically degraded completely by 30 min illumination with a 375W medium pressure mercury lamp. Adding a small amount of H2O2 or Fe3+, the efficiencies of photocatalytic degradation of X3B azo dye were increased rapidly. The mechanisms of the reaction and the role of the additives were also investigated. After 120 hours TiO2/beads showed no significant loss of the photocatalytic activity.

Lung cancer cell identification based on artificial neural network ensembles.

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.