Carbon nitride thin films as all-in-one technology for photocatalysis

Organic π-conjugated polymers are promising heterogeneous photocatalysts that involve photoredox or energy transfer processes. In such settings, the materials are usually applied in the form of dis..

Machine learning to refine decision making within a syndromic surveillance service

Background: Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms. Methods: A record of the risk assessment process was obtained from Public Health England for all 67505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes. Results: The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system. Conclusions: Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process

Growth and mass wasting of volcanic centers in the northern South Sandwich arc, South Atlantic, revealed by new multibeam mapping

New multibeam (swath) bathymetric sonar data acquired using an EM120 system on the RRS James Clark Ross, supplemented by sub-bottom profiling, reveals the underwater morphology of a not, vert, similar 12,000 km2 area in the northern part of the mainly submarine South Sandwich volcanic arc. The new data extend between 55° 45′S and 57° 20′S and include Protector Shoal and the areas around Zavodovski, Visokoi and the Candlemas islands groups. Each of these areas is a discrete volcanic center. The entirely submarine Protector Shoal area, close to the northern limit of the arc, forms a 55 km long east–west-trending seamount chain that is at least partly of silicic composition. The seamounts are comparable to small subaerial stratovolcanoes in size, with volumes up to 83 km3, indicating that they are the product of multiple eruptions over extended periods. Zavodovski, Visokoi and the Candlemas island group are the summits of three 3–3.5 km high volcanic edifices. The bathymetric data show evidence for relationships between constructional volcanic features, including migrating volcanic centers, structurally controlled constructional ridges, satellite lava flows and domes, and mass wasting of the edifices. Mass wasting takes place mainly by strong erosion at sea level, and dispersal of this material along chutes, probably as turbidity currents and other mass flows that deposit in extensive sediment wave fields. Large scale mass wasting structures include movement of unconsolidated debris in slides, slumps and debris avalanches. Volcanism is migrating westward relative to the underlying plate and major volcanoes are asymmetrical, being steep with abundant recent volcanism on their western flanks, and gently sloping with extinct, eroded volcanic sequences to their east. This is consistent with the calculated rate of subduction erosion of the fore-arc

Frizzled-7 is required for Wnt signaling in gastric tumours with and without Apc mutations

A subset of patients with gastric cancer have mutations in genes that participate in or regulate Wnt signaling at the level of ligand (Wnt) receptor (Fzd) binding. Moreover, increased Fzd expression is associated with poor clinical outcome. Despite these findings, there are no in vivo studies investigating the potential of targeting Wnt receptors for treating gastric cancer, and the specific Wnt receptor transmitting oncogenic Wnt signaling in gastric cancer is unknown. Here, we use inhibitors of Wnt/Fzd (OMP-18R5/vantictumab) and conditional gene deletion to test the therapeutic potential of targeting Wnt signaling in preclinical models of intestinal-type gastric cancer and ex vivo organoid cultures. Pharmacologic targeting of Fzd inhibited the growth of gastric adenomas in vivo. We identified Fzd7 to be the predominant Wnt receptor responsible for transmitting Wnt signaling in human gastric cancer cells and mouse models of gastric cancer, whereby Fzd7-deficient cells were retained in gastric adenomas but were unable to respond to Wnt signals and consequently failed to proliferate. Genetic deletion of Fzd7 or treatment with vantictumab was sufficient to inhibit the growth of gastric adenomas with or without mutations to Apc. Vantictumab is currently in phase Ib clinical trials for advanced pancreatic, lung, and breast cancer. Our data extend the scope of patients that may benefit from this therapeutic approach as we demonstrate that this drug will be effective in treating patients with gastric cancer regardless of APC mutation status