Spin- and angle-resolved photoemission studies of the electronic structure of Si(110)"16x2" surfaces

The electronic structure of Si(110)"16 x 2" double-domain, single-domain and 1 x 1 surfaces have been investigated using spin- and angle-resolved photoemission at sample temperatures of 77 K and 300 K. Angle-resolved photoemission was conducted using horizontally- and vertically-polarised 60 eV and 80 eV photons. Band-dispersion maps revealed four surface states ($S_1$ to $S_4$) which were assigned to silicon dangling bonds on the basis of measured binding energies and photoemission intensity changes between horizontal and vertical light polarisations. Three surface states ($S_1$, $S_2$ and $S_4$), observed in the Si(110)"16 x 2" reconstruction, were assigned to Si adatoms and Si atoms present at the edges of the corrugated terrace structure. Only one of the four surface states, $S_3$, was observed in both the Si(110)"16 x 2" and 1 x 1 band maps and consequently attributed to the pervasive Si zigzag chains that are components of both the Si(110)"16 x 2" and 1 x 1 surfaces. A state in the bulk-band region was attributed to an in-plane bond. All data were consistent with the adatom-buckling model of the Si(110)"16 x 2" surface. Whilst room temperature measurements of $P_y$ and $P_z$ were statistically compatible with zero, $P_x$ measurements of the enantiomorphic A-type and B-type Si(110)"16 x 2" surfaces gave small average polarisations of around 1.5\% that were opposite in sign. Further measurements at 77 K on A-type Si(110)"16 x 2" surface gave a smaller value of +0.3\%. An upper limit of $\sim1\%$ may thus be taken for the longitudinal polarisation.Comment: Main paper: 12 pages and 11 figures. Supplemental information: 5 pages and 2 figure

Low prevalence of Epstein–Barr virus in incident gastric adenocarcinomas from the United Kingdom

Epstein–Barr virus has been associated with a proportion of typical gastric adenocarcinomas. Here we report that the prevalence of Epstein–Barr virus in gastric adenocarcinomas from the United Kingdom is one of the lowest in the World. Gastric adenocarcinoma is another tumour whose association with Epstein–Barr virus varies with the population studied

Bridging the gap between high and low performing pupils through performance learning online analysis and curricula

Metacognition is a neglected area of investment in formal education and in teachers’ professional development. This paper presents an approach and tools, created by a London-based company called Performance Learning Education (PL), for supporting front-line teachers and learners in developing metacognitive competencies. An iterative process adopted by PL in developing and validating its approach is presented, demonstrating its value to real educational practices, it’s research potential in the area of metacognition, and its AI readiness, especially in relation to modelling learners’ non-cognitive competencies

A Protein Scaffold Coordinates SRC-Mediated JNK Activation in Response to Metabolic Stress

Obesity is a major risk factor for the development of metabolic syndrome and type 2 diabetes. How obesity contributes to metabolic syndrome is unclear. Free fatty acid (FFA) activation of a non-receptor tyrosine kinase (SRC)-dependent cJun NH2-terminal kinase (JNK) signaling pathway is implicated in this process. However, the mechanism that mediates SRC-dependent JNK activation is unclear. Here, we identify a role for the scaffold protein JIP1 in SRC-dependent JNK activation. SRC phosphorylation of JIP1 creates phosphotyrosine interaction motifs that bind the SH2 domains of SRC and the guanine nucleotide exchange factor VAV. These interactions are required for SRC-induced activation of VAV and the subsequent engagement of a JIP1-tethered JNK signaling module. The JIP1 scaffold protein, therefore, plays a dual role in FFA signaling by coordinating upstream SRC functions together with downstream effector signaling by the JNK pathway

Differing views - can chimpanzees do level 2 perspective-taking?

We gratefully acknowledge financial support by the German National Academic Foundation.Although chimpanzees understand what others may see, it is unclear if they understand how others see things (Level 2 perspective-taking). We investigated whether chimpanzees can predict the behavior of a conspecific which is holding a mistaken perspective that differs from their own. The subject competed with a conspecific over two food sticks. While the subject could see that both were the same size, to the competitor one appeared bigger than the other. In a previously established game, the competitor chose one stick in private first and the subject chose thereafter, without knowing which of the sticks was gone. Chimpanzees and 6-year-old children chose the ‘riskier’ stick (that looked bigger to the competitor) significantly less in the game than in a nonsocial control. Children chose randomly in the control, thus showing Level 2 perspective-taking skills; in contrast, chimpanzees had a preference for the ‘riskier’ stick here, rendering it possible that they attributed their own preference to the competitor to predict her choice. We thus run a follow-up in which chimpanzees did not have a preference in the control. Now they also chose randomly in the game. We conclude that chimpanzees solved the task by attributing their own preference to the other, while children truly understood the other’s mistaken perspective.Publisher PDFPeer reviewe

Predicting iron exceedance risk in drinking water distribution systems using machine learning

A Machine Learning approach has been developed to predict iron threshold exceedances in sub-regions of a drinking water distribution network from data collected the previous year. Models were trained using parameters informed by Self-Organising Map analysis based on ten years of water quality sampling data, pipe data and discolouration customer contacts from a UK network supplying over 2.3 million households. Twenty combinations of input parameters (network conditions) and three learning algorithms (Random Forests, Support Vector Machines and RUSBoost Trees) were tested. The best performing model was found to be Random Forests with input parameters of iron, turbidity, 3-day Heterotrophic Plate Counts, and high priority dead ends per District Metered Area. Different exceedance levels were tested and prediction accuracies of above 70% were achieved for UK regulatory concentration of 200 µg/L. Predicted probabilities per network sub-region were used to provide relative risk ranking to inform proactive management and investment decisions