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Sediments of time : environment and society in Chinese history
Written by some of the world's leading experts, 'Sediments of Time' offers a survey of the environmental history of China. Crystallising a new and distinct field of research, the book studies the effect of human social systems on the natural world.
Book
Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications
Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.
Journal Article
Effects of SGLT2 inhibitors on stroke and its subtypes in patients with type 2 diabetes: a systematic review and meta-analysis
Sodium-glucose cotransporter 2 (SGLT2) inhibitors have shown impressive effects in reducing major vascular events in several randomized controlled trials (RCTs). The purpose of this study was to perform a meta-analysis to evaluate the effect of SGLT2 inhibitors on the risk of stroke and its subtypes. All data from prospective RCTs up to 20 October 2020 involving SGLT2 inhibitors that reported stroke events as the primary endpoint or safety in subjects with type 2 diabetes were subjected to meta-analysis. Five eligible RCTs (EMPA-REG, CANVAS, DECLARE-TIMI 58, CREDENCE and VERTIS CV) involving 46,969 participants were included. Pooled analysis of the RCTs showed no significant effect of SGLT2 inhibitors on total stroke [risk ratio (RR) = 0.95; 95% confidence interval (CI) 0.79–1.13, P = 0.585]. Subgroup analysis indicated that SGLT2 inhibitors had no significant effect against fatal stroke, non-fatal stroke, ischemic stroke or transient ischemic attack. When only hemorrhagic stroke was included, SGLT2 inhibitors were associated with a significant 50% reduction compared with placebo (RR = 0.49, 95% CI 0.30–0.82, P = 0.007). This meta-analysis shows that SGLT2 inhibitors have a neutral effect on the risk of stroke and its subtypes but a potential protective effect against hemorrhagic stroke.
Journal Article
Assessing Perceptual Load and Cognitive Load by Fixation-Related Information of Eye Movements
Assessing mental workload is imperative for avoiding unintended negative consequences in critical situations such as driving and piloting. To evaluate mental workload, measures of eye movements have been adopted, but unequivocal results remain elusive, especially those related to fixation-related parameters. We aimed to resolve the discrepancy of previous results by differentiating two kinds of mental workload (perceptual load and cognitive load) and manipulated them independently using a modified video game. We found opposite effects of the two kinds of mental workload on fixation-related parameters: shorter fixation durations and more fixations when participants played an episode with high (vs. low) perceptual load, and longer fixation durations and fewer fixations when they played an episode with high (vs. low) cognitive load. Such opposite effects were in line with the load theory and demonstrated that fixation-related parameters can be used to index mental workload at different (perceptual and cognitive) stages of mental processing.
Journal Article
Comparing machine learning with case-control models to identify confirmed dengue cases
In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x10 3 / μL )], fever (≥38°C), low platelet counts [< 100 (x10 3 / μL )], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.
Journal Article
IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery
Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning. We propose an end-to-end trainable convolutional-deconvolutional deep neural network architecture that enables learning mapping from a single aerial imagery to a DSM for analysis of urban scenes. We perform multisensor fusion of aerial optical and aerial light detection and ranging (Lidar) data to prepare the training data for our pipeline. The dataset quality is key to successful estimation performance. Typically, a substantial amount of misregistration artifacts are present due to georeferencing/projection errors, sensor calibration inaccuracies, and scene changes between acquisitions. To overcome these issues, we propose a registration procedure to improve Lidar and optical data alignment that relies on Mutual Information, followed by Hough transform-based validation step to adjust misregistered image patches. We validate our building height estimation model on a high-resolution dataset captured over central Dublin, Ireland: Lidar point cloud of 2015 and optical aerial images from 2017. These data allow us to validate the proposed registration procedure and perform 3D model reconstruction from single-view aerial imagery. We also report state-of-the-art performance of our proposed architecture on several popular DSM estimation datasets.
Journal Article
Effect of Probiotics and Prebiotics on Immune Response to Influenza Vaccination in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
We conducted a meta-analysis to evaluate the effects of probiotics and prebiotics on the immune response to influenza vaccination in adults. We conducted a literature search of Pubmed, Embase, the Cochrane Library, the Cumulative Index to Nursing and Allied Health (CINAHL), Airiti Library, and PerioPath Index to Taiwan Periodical Literature in Taiwan. Databases were searched from inception to July 2017. We used the Cochrane Review risk of bias assessment tool to assess randomized controlled trial (RCT) quality. A total of 20 RCTs comprising 1979 adults were included in our systematic review. Nine RCTs including 623 participants had sufficient data to be pooled in a meta-analysis. Participants who took probiotics or prebiotics showed significant improvements in the H1N1 strain seroprotection rate (with an odds ratio (OR) of 1.83 and a 95% confidence interval (CI) of 1.19–2.82, p = 0.006, I2 = 0%), the H3N2 strain seroprotection rate (OR = 2.85, 95% CI = 1.59–5.10, p < 0.001, I2 = 0%), and the B strain seroconversion rate (OR = 2.11, 95% CI = 1.38–3.21, p < 0.001, I2 = 0%). This meta-analysis suggested that probiotics and prebiotics are effective in elevating immunogenicity by influencing seroconversion and seroprotection rates in adults inoculated with influenza vaccines.
Journal Article
Incoherent Digital Holography: A Review
Digital holography (DH) is a promising technique for modern three-dimensional (3D) imaging. Coherent holography records the complex amplitude of a 3D object holographically, giving speckle noise upon reconstruction and presenting a serious drawback inherent in coherent optical systems. On the other hand, incoherent holography records the intensity distribution of the object, allowing a higher signal-to-noise ratio as compared to its coherent counterpart. Currently there are two incoherent digital holographic techniques: optical scanning holography (OSH) and Fresnel incoherent correlation holography (FINCH). In this review, we first explain the principles of OSH and FINCH. We then compare, to some extent, the differences between OSH and FINCH. Finally, some of the recent applications of the two incoherent holographic techniques are reviewed.
Journal Article
Strong signature of electron-vibration coupling in molecules on Ag(111) triggered by tip-gated discharging
Electron-vibration coupling is of critical importance for the development of molecular electronics, spintronics, and quantum technologies, as it affects transport properties and spin dynamics. The control over charge-state transitions and subsequent molecular vibrations using scanning tunneling microscopy typically requires the use of a decoupling layer. Here we show the vibronic excitations of tetrabromotetraazapyrene (TBTAP) molecules directly adsorbed on Ag(111) into an orientational glassy phase. The electron-deficient TBTAP is singly-occupied by an electron donated from the substrate, resulting in a spin 1/2 state, which is confirmed by a Kondo resonance. The TBTAP
•−
discharge is controlled by tip-gating and leads to a series of peaks in scanning tunneling spectroscopy. These occurrences are explained by combining a double-barrier tunneling junction with a Franck-Condon model including molecular vibrational modes. This work demonstrates that suitable precursor design enables gate-dependent vibrational excitations of molecules on a metal, thereby providing a method to investigate electron-vibration coupling in molecular assemblies without a decoupling layer.
Electron-vibration coupling is driving advances in molecular electronics, spintronics, and quantum technology. Here, the authors succeeded in directly controlling vibronic excitations in tetrabromotetraazapyrene (TBTAP) molecules on the surface of Ag(111).
Journal Article