Dislocation motion and instability

The Peach-Koehler expression for the stress generated by a single (non-planar) curvilinear dislocation is evaluated to calculate the dislocation self stress. This is combined with a law of motion to give the self-induced motion of a general dislocation curve. A stability analysis of a rectilinear, uniformly translating dislocation is then performed. The dislocation is found to be susceptible to a helical instability, with the maximum growth rate occurring when the dislocation is almost, but not exactly, pure screw. The non-linear evolution of the instability is determined numerically, and implications for slip band formation and non-Schmid behaviour in yielding discussed

Proton pump inhibitors induced fungal dysbiosis in patients with gastroesophageal reflux disease

Gut mycobiota inhabits human gastrointestinal lumen and plays a role in human health and disease. We investigated the influence of proton pump inhibitors (PPIs) on gastric mucosal and fecal mycobiota in patients with gastroesophageal reflux diseases (GERD) by using Internal Transcribed Spacer 1 sequencing. A total of 65 participants were included, consisting of the healthy control (HC) group, GERD patients who did not use PPIs (nt-GERD), and GERD patients who used PPIs, which were further divided into short-term (s-PPI) and long-term PPI user (l-PPI) groups based on the duration of PPI use. The alpha diversity and beta diversity of gastric mucosal mycobiota in GERD patients with PPI use were significantly different from HCs, but there were no differences between s-PPI and l-PPI groups. LEfSe analysis identified Candida at the genus level as a biomarker for the s-PPI group when compared to the nt-GERD group. Meanwhile, Candida, Nothojafnea, Rhizodermea, Ambispora, and Saccharicola were more abundant in the l-PPI group than in the nt-GERD group. Furthermore, colonization of Candida in gastric mucosa was significantly increased after PPI treatment. However, there was no significant difference in Candida colonization between patients with endoscopic esophageal mucosal breaks and those without. There were significant differences in the fecal mycobiota composition between HCs and GERD patients regardless whether or not they used PPI. As compared to nt-GERD patient samples, there was a high abundance of Alternaria, Aspergillus, Mycenella, Exserohilum, and Clitopilus in the s-PPI group. In addition, there was a significantly higher abundance of Alternaria, Aspergillus, Podospora, Phallus, and Monographella in the l-PPI group than nt-GERD patients. In conclusion, our study indicates that dysbiosis of mycobiota was presented in GERD patients in both gastric mucosal and fecal mycobiota. PPI treatment may increase the colonization of Candida in the gastric mucosa in GERD patients

Cisplatin inhibits the proliferation of Saos-2 osteosarcoma cells via the miR-376c/TGFA pathway

The transforming growth factor alpha (TGFA) gene is involved in the proliferation and metastasis of various tumors, but its role in cell sensitivity to cisplatin chemotherapy is unclear. In this study, we investigated the mechanism underlying inhibitory effects of cisplatin on growth and proliferation of osteosarcoma cells. Osteosarcoma and normal skeletal muscle tissues were collected from 26 patients by biopsy. TGFA was silenced or overexpressed in Saos-2 osteosarcoma cells by transfection with TGFA-shRNA or TGFA ORF clone, respectively. MiR-376c was inhibited or overexpressed by transfection of Saos-2 cells with miR-376c sponge or miR-376c mimics, respectively. Cell growth was analyzed by MTT assay and cell proliferation by BrdU assay. MiR-376c and TGFA mRNA expression was detected by quantitative reverse transcription PCR and TGFA protein expression by Western blot. The target relationship between miR-376c and TGFA was assessed by luciferase reporter assay. Both in osteosarcoma tissues and Saos-2 cells, miR-376c expression was significantly decreased and TGFA mRNA expression was significantly increased compared with control. Transfection of Saos-2 cells with TGFA-shRNA silenced TGFA expression and significantly inhibited cell growth and proliferation. TGFA mRNA and protein expression in Saos-2 cells significantly decreased with increasing cisplatin concentrations (2.5–10 mg/L). Transfection with TGFA ORF clone reversed the inhibitory effects of cisplatin on Saos-2 cell proliferation. Compared with cisplatin (10 mg/L) treatment alone, the combined treatment with cisplatin and miR-376c mimics inhibited the proliferation of Saos-2 cells more significantly. MiR-376c suppressed TGFA expression by directly interacting with its 3' UTR region. Overall, cisplatin inhibited the proliferation of Saos-2 cells by upregulating miR-376c and downregulating TGFA expression

Fabrication of TiO 2

The TiO2 photoelectrodes fabricated on the substrate of Ti foils by Ti ions implantation and subsequent annealing at different temperatures were applied for water splitting. The size of TiO2 nanoparticles increased with annealing temperatures, and the GIXRD patterns and Raman spectra demonstrate that the phase of TiO2 turns to rutile at high temperature. The photoelectrochemical (PEC) and X-ray photoelectron spectroscopy (XPS) spectra of the valence band demonstrate that the samples annealed at 400 and 500°C show the n-type property. The sample annealed at 600°C shows the weak p-type TiO2 property. For the sample annealed at 700°C, the negative photocurrent is main, which mainly performs the p-type property of TiO2. The IPCE values indicate that the absorption edges are red shifted with the increase of annealing temperatures

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

H3K27ME3-RICH GENOMIC REGIONS CAN FUNCTION AS SILENCERS TO REPRESS GENE EXPRESSION VIA CHROMATIN INTERACTIONS

Ph.DDOCTOR OF PHILOSOPHY (FOS