Topological Defects Coupling Smectic Modulations to Intra-unit-cell Nematicity in Cuprate

We study the coexisting smectic modulations and intra-unit-cell nematicity in the pseudogap states of underdoped Bi2Sr2CaCu2O8+{\delta}. By visualizing their spatial components separately, we identified 2\pi topological defects throughout the phase-fluctuating smectic states. Imaging the locations of large numbers of these topological defects simultaneously with the fluctuations in the intra-unit-cell nematicity revealed strong empirical evidence for a coupling between them. From these observations, we propose a Ginzburg-Landau functional describing this coupling and demonstrate how it can explain the coexistence of the smectic and intra-unit-cell broken symmetries and also correctly predict their interplay at the atomic scale. This theoretical perspective can lead to unraveling the complexities of the phase diagram of cuprate high-critical-temperature superconductors

Commensurate 4a04a_0 period Charge Density Modulations throughout the Bi2Sr2CaCu2O8+xBi_2Sr_2CaCu_2O_{8+x} Pseudogap Regime

Theories based upon strong real space (r-space) electron electron interactions have long predicted that unidirectional charge density modulations (CDM) with four unit cell (4$a_0$) periodicity should occur in the hole doped cuprate Mott insulator (MI). Experimentally, however, increasing the hole density p is reported to cause the conventionally defined wavevector $Q_A$ of the CDM to evolve continuously as if driven primarily by momentum space (k-space) effects. Here we introduce phase resolved electronic structure visualization for determination of the cuprate CDM wavevector. Remarkably, this new technique reveals a virtually doping independent locking of the local CDM wavevector at $|Q_0|=2\pi/4a_0$ throughout the underdoped phase diagram of the canonical cuprate $Bi_2Sr_2CaCu_2O_8$. These observations have significant fundamental consequences because they are orthogonal to a k-space (Fermi surface) based picture of the cuprate CDM but are consistent with strong coupling r-space based theories. Our findings imply that it is the latter that provide the intrinsic organizational principle for the cuprate CDM state

Composite 3D printing of biomimetic human teeth

Human teeth are mechanically robust through a complex structural composite organisation of materials and morphology. Efforts to replicate mechanical function in artificial teeth (typodont teeth), such as in dental training applications, attempt to replicate the structure and morphology of real teeth but lack tactile similarities during mechanical cutting of the teeth. In this study, biomimetic typodont teeth, with morphology derived from X-ray microtomography scans of extracted teeth, were 3D printed using an approach to develop novel composites. These composites with a range of glass, hydroxyapatite and porcelain reinforcements within a methacrylate-based photopolymer resin were compared to six commercial artificial typodont teeth. Mechanical performance of the extracted human teeth and 3D printed typodont teeth were evaluated using a haptic approach of measuring applied cutting forces. Results indicate 3D printed typodont teeth replicating enamel and dentine can be mechanically comparable to extracted human teeth despite the material compositions differing from the materials found in human teeth. A multiple parameter variable of material elastic modulus and hardness is shown to describe the haptic response when cutting through both human and biomimetic, highlighting a critical dependence between the ratio of material mechanical properties and not absolute material properties in determining tooth mechanical performance under the action of cutting forces

Machine Learning in Electronic Quantum Matter Imaging Experiments

Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.Comment: 44 pages, 15 figure

Accurate modelling of language learning tasks and students using representations of grammatical proficiency

Adaptive learning systems aim to learn the relationship between curriculum content and students in order to optimise a student’s learning process. One form of such a system is content recommendation in which the system attempts to predict the most suitable content to next present to the student. In order to develop such a system, we must learn reliable representations of the curriculum content and the student. We consider this in the context of foreign language learning and present a novel neural network architecture to learn such representations. We also show that by incorporating grammatical error distributions as a feature in our neural architecture, we can substantially improve the quality of our representations. Different types of grammatical error are automatically detected in essays submitted by students to an online learning platform. We evaluate our model and representations by predicting student scores and grammatical error distributions on unseen language tasks. We also discuss further uses for our model beyond content recommendation such as inferring student knowledge components for a given domain and optimising spacing and repetition of content for efficient long term retention.Cambridge Assessmen

Montreal Cognitive Assessment for the detection of dementia

Background: Dementia is a progressive syndrome of global cognitive impairment with significant health and social care costs. Global prevalence is projected to increase, particularly in resource‐limited settings. Recent policy changes in Western countries to increase detection mandates a careful examination of the diagnostic accuracy of neuropsychological tests for dementia. Objectives: To determine the accuracy of the Montreal Cognitive Assessment (MoCA) for the detection of dementia. Search methods: We searched MEDLINE, EMBASE, BIOSIS Previews, Science Citation Index, PsycINFO and LILACS databases to August 2012. In addition, we searched specialised sources containing diagnostic studies and reviews, including MEDION (Meta‐analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C‐EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence‐based Laboratory Medicine) databases. We also searched ALOIS (Cochrane Dementia and Cognitive Improvement Group specialized register of diagnostic and intervention studies). We identified further relevant studies from the PubMed ‘related articles’ feature and by tracking key studies in Science Citation Index and Scopus. We also searched for relevant grey literature from the Web of Science Core Collection, including Science Citation Index and Conference Proceedings Citation Index (Thomson Reuters Web of Science), PhD theses and contacted researchers with potential relevant data. // Selection criteria: Cross‐sectional designs where all participants were recruited from the same sample were sought; case‐control studies were excluded due to high chance of bias. We searched for studies from memory clinics, hospital clinics, primary care and community populations. We excluded studies of early onset dementia, dementia from a secondary cause, or studies where participants were selected on the basis of a specific disease type such as Parkinson’s disease or specific settings such as nursing homes. // Data collection and analysis: We extracted dementia study prevalence and dichotomised test positive/test negative results with thresholds used to diagnose dementia. This allowed calculation of sensitivity and specificity if not already reported in the study. Study authors were contacted where there was insufficient information to complete the 2x2 tables. We performed quality assessment according to the QUADAS‐2 criteria. Methodological variation in selected studies precluded quantitative meta‐analysis, therefore results from individual studies were presented with a narrative synthesis. // Main results: Seven studies were selected: three in memory clinics, two in hospital clinics, none in primary care and two in population‐derived samples. There were 9422 participants in total, but most of studies recruited only small samples, with only one having more than 350 participants. The prevalence of dementia was 22% to 54% in the clinic‐based studies, and 5% to 10% in population samples. In the four studies that used the recommended threshold score of 26 or over indicating normal cognition, the MoCA had high sensitivity of 0.94 or more but low specificity of 0.60 or less. // Authors' conclusions: The overall quality and quantity of information is insufficient to make recommendations on the clinical utility of MoCA for detecting dementia in different settings. Further studies that do not recruit participants based on diagnoses already present (case‐control design) but apply diagnostic tests and reference standards prospectively are required. Methodological clarity could be improved in subsequent DTA studies of MoCA by reporting findings using recommended guidelines (e.g. STARDdem). Thresholds lower than 26 are likely to be more useful for optimal diagnostic accuracy of MoCA in dementia, but this requires confirmation in further studies

Electrophysiological Signatures of Spatial Boundaries in the Human Subiculum.

Environmental boundaries play a crucial role in spatial navigation and memory across a wide range of distantly related species. In rodents, boundary representations have been identified at the single-cell level in the subiculum and entorhinal cortex of the hippocampal formation. Although studies of hippocampal function and spatial behavior suggest that similar representations might exist in humans, boundary-related neural activity has not been identified electrophysiologically in humans until now. To address this gap in the literature, we analyzed intracranial recordings from the hippocampal formation of surgical epilepsy patients (of both sexes) while they performed a virtual spatial navigation task and compared the power in three frequency bands (1-4, 4-10, and 30-90 Hz) for target locations near and far from the environmental boundaries. Our results suggest that encoding locations near boundaries elicited stronger theta oscillations than for target locations near the center of the environment and that this difference cannot be explained by variables such as trial length, speed, movement, or performance. These findings provide direct evidence of boundary-dependent neural activity localized in humans to the subiculum, the homolog of the hippocampal subregion in which most boundary cells are found in rodents, and indicate that this system can represent attended locations that rather than the position of one\u27s own body

Approaches to 3D printing teeth from X-ray microtomography.

Artificial teeth have several advantages in preclinical training. The aim of this study is to three-dimensionally (3D) print accurate artificial teeth using scans from X-ray microtomography (XMT). Extracted and artificial teeth were imaged at 90 kV and 40 kV, respectively, to create detailed high contrast scans. The dataset was visualised to produce internal and external meshes subsequently exported to 3D modelling software for modification before finally sending to a slicing program for printing. After appropriate parameter setting, the printer deposited material in specific locations layer by layer, to create a 3D physical model. Scans were manipulated to ensure a clean model was imported into the slicing software, where layer height replicated the high spatial resolution that was observed in the XMT scans. The model was then printed in two different materials (polylactic acid and thermoplastic elastomer). A multimaterial print was created to show the different physical characteristics between enamel and dentine