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Abstract The excellent outcomes seen in patients treated with adjuvant trastuzumab emtansine (T-DM1) in the ATEMPT trial and the favorable toxicity profile associated with this agent make T-DM1 a potential therapeutic option for select patients with stage I HER2-positive breast cancer. Moreover, T-DM1 is an established adjuvant treatment for patients with HER2-positive breast cancer with the residual invasive disease after neoadjuvant therapy. Given that cardiotoxicity is the most significant adverse event of trastuzumab, which is a main molecular component of T-DM1, we conducted a sub-analysis of the ATEMPT trial to determine the cardiac safety of adjuvant T-DM1. In this analysis, the incidence of grade 3–4 left ventricular systolic dysfunction (LVSD) in T-DM1 or trastuzumab plus paclitaxel arms were respectively 0.8 and 1.8%. In addition, three (0.8%) patients in the T-DM1 arm and six (5.3%) patients in the adjuvant paclitaxel with trastuzumab (TH) arm experienced a significant asymptomatic left ventricular ejection fraction (LVEF) decline that per-protocol required holding T-DM1 or trastuzumab. All patients with available follow-up data experienced full resolution of cardiac symptoms and LVEF normalization. Furthermore, we performed an exploratory analysis to assess the relationship between age, baseline LVEF, and body mass index with cardiac outcomes. No significant association between these baseline characteristics and the incidence of significant asymptomatic LVEF decline or symptomatic LVSD was identified. The low incidence of significant cardiac adverse events in this population during therapy with adjuvant T-DM1 suggests that studies on the cost-effectiveness of cardiac monitoring during adjuvant therapy using anthracycline-free regimens are needed. Clinical Trial Registration: ClinicalTrials.gov, NCT01853748

Diffusion of native defects such as vacancies and their interactions with impurities are fundamental to semiconductor crystal growth, device processing, and design. However, the transient equilibration of native defects is difficult to directly measure. We used (AlxGa1−x)2O3/Ga2O3 superlattices (SLs) to detect and analyze transient diffusion of cation vacancies during annealing in O2 at 1000–1100 °C. Using a novel finite difference scheme for diffusion with time- and space-varying diffusion constants, we determined diffusion constants for Al, Fe, and cation vacancies, including the vacancy concentration dependence for Al. In the case of SLs grown on Sn-doped β-Ga2O3 (010) substrates, gradients observed in the extent of Al diffusion indicate a supersaturation of vacancies in the substrates that transiently diffuse through the SLs coupled strongly to Sn and thus slowed compared to undoped cases. In the case of SLs grown on (010) Fe-doped substrates, the Al diffusion is uniform through the SLs, indicating a depth-uniform concentration of vacancies. We find no evidence for the introduction of VGa from the free surface at rates sufficient to affect Al diffusion at at. % concentrations, establishing an upper bound on surface injection. In addition, we show that unintentional impurities in Sn-doped Ga2O3 such as Fe, Ni, Mn, Cu, and Li also diffuse toward the surface and accumulate. Many of these likely have fast interstitial diffusion modes capable of destabilizing devices, thus suggesting that impurities may require further reduction. This work provides a method to measure transients in diffusion-mediating native defects otherwise hidden in common processes such as ion implantation, etching, and film growth.

Abstract Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validated. Here we propose AI-Endo, an intelligent surgical workflow recognition suit, for endoscopic submucosal dissection (ESD). Our AI-Endo is trained on high-quality ESD cases from an expert endoscopist, covering a decade time expansion and consisting of 201,026 labeled frames. The learned model demonstrates outstanding performance on validation data, including cases from relatively junior endoscopists with various skill levels, procedures conducted with different endoscopy systems and therapeutic skills, and cohorts from international multi-centers. Furthermore, we integrate our AI-Endo with the Olympus endoscopic system and validate the AI-enabled cognitive assistance system with animal studies in live ESD training sessions. Dedicated data analysis from surgical phase recognition results is summarized in an automatically generated report for skill assessment.

Abstract The Hospitality and Food Service (HaFS) sectors are notoriously known for their contribution to the food waste problem. Hence, there is an urgent need to devise strategies to reduce food waste in the HaFS sectors and to decarbonise their operation to help fight hunger, achieve food security, improve nutrition and mitigate climate change. This study proposes three streams to decarbonise the staff cafeteria operation in an integrated resort in Macau. These include upstream optimisation to reduce unserved food waste, midstream education to raise awareness amongst staff about the impact of food choices on the climate and health, and finally downstream recognition to reduce edible plate waste using a state-of-the-art computer vision system. Technology can be an effective medium to facilitate desired behavioural change through nudging, much like how speed cameras can cause people to slow down and help save lives. The holistic and data-driven approach taken revealed great potential for organisations or institutions that offer catering services to reduce their food waste and associated carbon footprint whilst educating individuals about the intricate link between food, climate and well-being.

Abstract Bismuth telluride/iron telluride (Bi2Te3/Fe1+yTe) heterostructures are known to exhibit interfacial superconductivity between two non‐superconducting materials: Fe1+yTe as the parent compound of Fe‐based superconducting materials and the topological insulator Bi2Te3. Here, a top‐down approach is presented starting from 2D heterostructures to fabricate 1D Bi2Te3/Fe1+yTe nanowires or narrow nanoribbons. It is demonstrated that the Bi2Te3/Fe1+yTe heterostructure remains intact in nanostructures of widths on the order of 100 nm and the interfacial superconductivity is preserved, as evidenced by electrical transport and Andreev reflection point contact spectroscopy experiments measured at the end of the nanowire. The differential conductance shows a similar superconducting twin‐gap structure as in 2D heterostructures, but with enhanced fluctuation effects due to the lower dimensionality. A zero‐bias conductance peak indicates the presence of an Andreev bound state and, given the involvement of the topological Bi2Te3 surface state, a possible topological nature of superconductivity is discussed with strong interplay with an emerging ferromagnetism due to the interstitial excess iron (Fe) in the Fe1+yTe layer, developing in parallel with superconductivity at low temperatures.

Two algorithms set for automatic detection of bow shock (BS) and magnetopause (MP) boundaries at Saturn using in situ magnetic field and plasma data acquired by the Cassini spacecraft are presented. Traditional threshold-based and modern deep learning algorithms were investigated for the task of boundary detection. Sections of Cassini’s orbits were pre-selected based on empirical BS and MP boundary models, and from outlier detection in magnetic field data using an autoencoder neural network. The threshold method was applied to pre-selected magnetic field and plasma data independently to compute parameters to which a threshold was applied to determine the presence of a boundary. The deep learning method used a type of convolutional neural network (CNN) called ResNet on images of magnetic field time series data and electron energy-time spectrograms to classify the presence of boundaries. 2012 data were held out of the training data to test and compare the algorithms on unseen data. The comparison showed that the CNN method applied to plasma data outperformed the threshold method. A final multiclass CNN classifier trained on plasma data obtained F1 scores of 92.1% ± 1.4% for BS crossings and 84.7% ± 1.9% for MP crossings on a corrected test dataset (from use of a bootstrap method). Reliable automated detection of boundary crossings could enable future spacecraft experiments like the PEP instrument on the upcoming JUICE spacecraft mission to dynamically adapt the best observing mode based on rapid classification of the boundary crossings as soon as it appears, thus yielding higher quality data and improved potential for scientific discovery.

Background: One area of biomedical research concerns is applying new treatments to cure human diseases, moving bench-side research to the bedside practice. While using animal models is crucial in the research process, researchers should strictly adhere to the moral 4R framework to protect animal welfare—replacement, reduction, refinement, and responsibility. Virtual reality (VR) applies computer technology to create a simulated environment, allowing players to immerse and interact with animated 3D contexts. We developed a virtual animal-holding simulator (ViSi) using immersive virtual reality technology for students studying in the undergraduate biomedical sciences programme. The specific objectives of the paper are to 1) describe the development of the VR courseware for animal training and 2) describe the learning experience among students.Method and Result: An evaluation of the courseware was conducted among Year one and two biomedical sciences students. Students who participated in ViSi responded positively about their involvement in the virtual environment experience and their concentration on the assigned task.Discussion: ViSi is a reliable simulation technology that can train animal handling skills, which replaces real animals, while learners’ multi-cognition could still be enhanced with simulation training. Thus, the impact of immersive VR technology integrated into skills training is promising, although few technical problems are to be resolved.

Abstract In the absence of nationwide mass testing for an emerging health crisis, alternative approaches could provide necessary information efficiently to aid policy makers and health bodies when dealing with a pandemic. The following work presents a methodology by which Twitter data surrounding the first wave of the COVID-19 pandemic in the UK is harvested and analysed using two main approaches. The first is an investigation into localized outbreak predictions by developing a prototype early-warning system using the distribution of total tweet volume. The temporal lag between the rises in the number of COVID-19 related tweets and officially reported deaths by Public Health England (PHE) is observed to be 6–27 days for various UK cities which matches the temporal lag values found in the literature. To better understand the topics of discussion and attitudes of people surrounding the pandemic, the second approach is an in-depth behavioural analysis assessing the public opinion and response to government policies such as the introduction of face-coverings. Using topic modelling, nine distinct topics are identified within the corpus of COVID-19 tweets, of which the themes ranged from retail to government bodies. Sentiment analysis on a subset of mask related tweets revealed sentiment spikes corresponding to major news and announcements. A Named Entity Recognition (NER) algorithm is trained and applied in a semi-supervised manner to recognise tweets containing location keywords within the unlabelled corpus and achieved a precision of 81.6%. Overall, these approaches allowed extraction of temporal trends relating to PHE case numbers, popular locations in relation to the use of face-coverings, and attitudes towards face-coverings, vaccines and the national ‘Test and Trace’ scheme.

Introduction: There is a lacking of tunnel length measurements data for primary anterior cruciate ligament (ACL) reconstruction in the Chinese population. Different tunnel lengths affect the security of the tendon graft fixation as well as bone–tendon healing. Long-term result may then be compromised. Double-bundle ACL reconstruction will work well if both grafts have good quality, size, and tendon–bone fixation. Proper rehabilitation is also an important element. However, the femoral tunnel length for posterolateral bundle will have a higher prevalence of inadequate graft bone contact because of its orientation, especially in the Chinese population with smaller knees compared with the Western population. Methods: From January 2009 to June 2011, 148 cases of ACL reconstruction have been performed in our hospital. After excluding cases with revision, isolated bundle (anteromedial or posterolateral), and four-strand hamstring graft single-bundle ACL reconstructions, we recruited 48 cases with primary double-bundle ACL reconstruction in the study of tunnel lengths. Hamstring tendon autografts (semitendinosus and gracilis) were used. A small cohort study with Level 3 evidence was carried out. Results: In 31.3% (15/48) of patients, inadequate graft–bone contact was observed in the posterolateral bundle using the available shortest Endobutton CL 15 (Endobutton continuous loop fixation device; Smith and Nephew, Andover, Massachusetts, USA) in femoral tunnel fixation. Conclusion: ACL reconstruction is a complex procedure that relies on good tendon graft length, graft size, tunnel length, and secure fixation method. Inadequate posterolateral tunnel length may contribute to short-term complications as well as long-term failure. Further refining of the choices of graft, better orientation of tunnels, and a better fixation method may be important, especially in the Chinese population with smaller knees.