Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms

Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 14 years of follow-up samples to predict the individual risk of developing dementia. The decision boundaries of the best source model are further updated by using an alternative dataset from a different and much younger population (n = 473, mean age = 52 years) to obtain an additional prediction model known as the target model. We further apply the SHapely Additive exPlanation (SHAP) algorithm to visualise the risk factors responsible for the prediction at both population and individual levels. The best source model achieves a geometric accuracy of 87%, specificity of 99%, and sensitivity of 76%. In comparison to a baseline model, our target model achieves better performance across several performance metrics, within an increase in geometric accuracy of 16.9%, specificity of 2.7%, and sensitivity of 19.1%, an area under the receiver operating curve (AUROC) of 11% and a transfer learning efficacy rate of 20.6%. The strength of our approach is the large sample size used in training the source model, transferring and applying the “knowledge” to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.
Stakeholders' Perspectives on the Challenges of Emergency Obstetric Referrals and the Feasibility and Acceptability of an mHealth Intervention in Northern Iraq

The health system in northern Iraq has been weakened by conflict and the internal displacement of over three million people. Mobile phone-based interventions (mHealth) may improve maternal and neonatal health outcomes by enabling emergency referrals, facilitating communication between patients and providers, and improving patient data management; however, they have not been sufficiently studied in conflict-affected settings. We explored stakeholders' perspectives on challenges to obstetric referrals and the feasibility and acceptability of a mobile phone-based application to reduce delays in reaching emergency obstetric care in order to inform its development. We conducted a qualitative study in the Kurdistan region of northern Iraq from May to July, 2018. Using purposive sampling, we carried out 15 semi-structured interviews with coordination actors including healthcare management staff, government health officials, non-government health program managers and ambulance staff. The interviews explored obstetric care delivery, referral processes, mobile phone usage and mHealth implementation strategies. Eleven focus group discussions, which incorporated participatory activities on similar topics, were conducted with ambulance drivers, hospital and primary health center staff. Audio-recorded, transcribed and translated data were coded iteratively to identify emerging concepts, and analyzed thematically. Sixty-eight stakeholders (36 women and 32 men) participated. Challenges regarding the referral system included resource limitations, security concerns, costs and women's reluctance to be transported in male-staffed ambulances. In terms of obstetric care and decision-making, participants noted gaps in communication and coordination of services with the current paper-based system between health care providers, ambulance drivers, and hospital staff. Ambulance drivers reported incurring delays through lack of patient information, poor road conditions, and security issues. A prototype mobile phone application was found to be acceptable based on perceived usefulness to address some of the challenges to safe obstetric care and focused on phone usage, access to information, Global Positioning System (GPS), connectivity, cost, and user-friendliness. However, the feasibility of the innovation was considered in relation to implementation challenges that were identified, including poor connectivity, and digital literacy. Implementation of the app would need to account for the humanitarian context, cultural and gender norms regarding obstetric care, and would require substantial commitment and engagement from policymakers and practitioners.
Depletion of Numb and Numblike in Murine Lung Epithelial Cells Ameliorates Bleomycin-Induced Lung Fibrosis by Inhibiting the β-Catenin Signaling Pathway

Idiopathic pulmonary fibrosis (IPF) represents the most aggressive form of pulmonary fibrosis (PF) and is a highly debilitating disorder with a poorly understood etiology. The lung epithelium seems to play a critical role in the initiation and progression of the disease. A repeated injury of lung epithelial cells prompts type II alveolar cells to secrete pro-fibrotic cytokines, which induces differentiation of resident mesenchymal stem cells into myofibroblasts, thus promoting aberrant deposition of extracellular matrix (ECM) and formation of fibrotic lesions. Reactivation of developmental pathways such as the Wnt-β-catenin signaling cascade in lung epithelial cells plays a critical role in this process, but the underlying mechanisms are still enigmatic. Here, we demonstrate that the membrane-associated protein NUMB is required for pathological activation of β-catenin signaling in lung epithelial cells following bleomycin-induced injury. Importantly, depletion of Numb and Numblike reduces accumulation of fibrotic lesions, preserves lung functions, and increases survival rates after bleomycin treatment of mice. Mechanistically, we demonstrate that NUMB interacts with casein kinase 2 (CK2) and relies on CK2 to activate β-catenin signaling. We propose that pharmacological inhibition of NUMB signaling may represent an effective strategy for the development of novel therapeutic approaches against PF.
Toward Patient-Specific Prediction of Ablation Strategies for Atrial Fibrillation Using Deep Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.
Ocean Circulation Model Applications for the Estuary-Coastal-Open Sea Continuum

Coastal zones are among the most variable environments. As such, they require adaptive water management to ensure the balance of economic and social interests with environmental concerns. High quality marine data of hydrographic conditions e.g., sea level, temperature, salinity, and currents are needed to provide a sound foundation for the decision making process. Operational models with sufficiently high forecasting quality and resolution can be used for a further extension of the marine service toward the coastal-estuary areas. The Limfjord is a large and shallow water body in Northern Jutland, connecting the North Sea in the West and the Kattegat in the East. It is currently not covered by the CMEMS service, despite its importance for sea shipping, aquaculture and mussel fisheries. In this study, we use the operational HIROMB-BOOS Model (HBM) to resolve the full Baltic-Limfjord-North Sea system with a horizontal resolution of 185.2 m in the Limfjord. The study shows several factors that are essential for successfully modeling the coastal-estuary system: (a) high computational efficiency and flexible grids to allow high resolution in the fjord, (b) an improved short wave radiation scheme to model the thermodynamics and the diurnal variability of the temperature in very shallow waters, (c) high resolution atmospheric forcing, (d) adequate river forcing, and (e) accurate bathymetry in the narrow straits. With properly resolving these issues, the system is able to provide high quality sea level forecast for storm surge warning and hydrography forecasts: temperature, salinity and currents with sufficiently good quality for ecosystem-based management. The model is able to simulate the complex spatial and temporal pattern of sea level, salinity and temperature in the Limfjord and to reproduce their diurnal, seasonal and interannual variability and stratification rather well. Its high computational efficiency makes it possible to model the transition from the basin-scales to coastal- and estuary-scales seamlessly. In total, The HBM model has been successfully extended, to include the complex estuary system of the Limfjord, and shows an adequate model performance with regards to sea level, salinity and temperature predictions, suitable for storm surge warning applications and coastal management applications.
Identification METTL18 as a Potential Prognosis Biomarker and Associated With Immune Infiltrates in Hepatocellular Carcinoma

Methyltransferase-like 18 (METTL18), a METTL family member, is abundant in hepatocellular carcinoma (HCC). Studies have indicated the METTL family could regulate the progress of diverse malignancies while the role of METTL18 in HCC remains unclear. Data of HCC patients were acquired from the cancer genome atlas (TCGA) and gene expression omnibus (GEO). The expression level of METTL18 in HCC patients was compared with normal liver tissues by Wilcoxon test. Then, the logistic analysis was used to estimate the correlation between METTL18 and clinicopathological factors. Besides, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to explore relevant functions and quantify the degree of immune infiltration for METTL18. Univariate and Multivariate Cox analyses and Kaplan–Meier analysis were used to estimate the association between METTL18 and prognosis. Besides, by cox multivariate analysis, a nomogram was conducted to forecast the influence of METTL18 on survival rates. METTL18-high was associated with Histologic grade, T stage, Pathologic stage, BMI, Adjacent hepatic tissue inflammation, AFP, Vascular invasion, and TP53 status (P < 0.05). HCC patients with METTL18-high had a poor Overall-Survival [OS; hazard ratio (HR): 1.87, P < 0.001), Disease-Specific Survival (DSS, HR: 1.76, P = 0.015), and Progression-Free Interval (PFI, HR: 1.51, P = 0.006). Multivariate analysis demonstrated that METTL18 was an independent factor for OS (HR: 2.093, P < 0.001), DSS (HR: 2.404, P = 0.015), and PFI (HR: 1.133, P = 0.006). Based on multivariate analysis, the calibration plots and C-indexes of nomograms showed an efficacious predictive effect for HCC patients. GSEA demonstrated that METTL18-high could activate G2M checkpoint, E2F targets, KRAS signaling pathway, and Mitotic Spindle. There was a positive association between the METTL18 and abundance of innate immunocytes (T helper 2 cells) and a negative relation to the abundance of adaptive immunocytes (Dendritic cells, Cytotoxic cells etc.). Finally, we uncovered knockdown of METTL18 significantly suppressed the proliferation, invasion, and migration of HCC cells in vitro. This research indicates that METTL18 could be a novel biomarker to evaluate HCC patients’ prognosis and an important regulator of immune responses in HCC.
Comments on “Decay of Ozone in Water: A Review” by D. Gardoni, A. Vailati, and R. Canziani (Ozone: Science & Engineering, 34(4): 233–242)

Recently, Gardoni et al. (2012) have offered a review of references devoted to ozone decomposition in water (or rather aqueous systems). They declared “to recapitulate the main results described in the literature” by considering a selection of 33 references (cf. Table 1, Gardoni et al. 2012) regarded by them as the main results available in the literature on that topic and published since 1913. I would like to make some comments on the authors’ presentation.
A Novel Model for Assessing e-Government Websites Using Hybrid Fuzzy Decision-Making Methods

Websites are considered as the core infrastructure of e-government, so evaluating the quality of websites assists organizations to provide high-quality online services to citizens. For this purpose, this paper is seeking to design a model that enables any organization to evaluate the quality of its websites and identify its strengths and weaknesses. The proposed model includes nine main indexes including “website design,” “responsiveness quality,” “security,” “content and information quality,” “participation,” “trust,” “maintenance and support,” “services” and “usability,” alongside with 85 indicators. Since some of indexes and indicators possess intrinsic uncertainties so “fuzzy set theory” was applied to model the problem’s ambiguity. “Analytic hierarchy process” and “PROMETHEE” methods were applied to weigh and rank indexes and indicators respectively. After designing the model, it was used for assessing the websites of five metropolitan municipalities of Iranian cities to spot their strengths and weaknesses.
Uncertainty of Drone-Derived DEMs and Significance of Detected Morphodynamics in Artificially Scraped Dunes

This work capitalises on the morphodynamic study of a scraped artificial dune built on the sandy beach of Porto Garibaldi (Comacchio, Italy) as a barrier to protect the touristic facilities from sea storms during the winter season and contributes to understanding of the role of elevation data uncertainty and uniform thresholds for change detection (TCDs) on the interpretation of volume change estimations. This application relies on products derived from unmanned aerial vehicle (UAV) surveys and on the evaluation of the uncertainty associated with volume change estimations to interpret the case study morphodynamics under non-extreme sea and wind conditions. The analysis was performed by comparing UAV-derived digital elevation models (DEMs)—root mean squared error (RMSE) vs. global navigation satellite system (GNSS) < 0.05 m—and orthophotos, considering the significance of the identified changes by applying a set of TCDs. In this case, a threshold of ~0.15 m was able to detect most of the morphological variations. The set of TCD ≤ 0.15 m was considered to discuss the significance of minor changes and the uncertainty of volume change calculations. During the analysed period (21 December 2016–20 January 2017), water levels and waves affected the front of the artificial dune by eroding the berm area; winds remodelled the entire dune, moving the loose sand around the dune and further inland; sediment volumes mobilised by sea and wind forcing were comparable. This work suggests that UAV-derived coastal morphological variations should be interpreted by integrating: (i) a set of uniform thresholds to detect significant changes; (ii) the uncertainty generated by the propagation of the original uncertainty of the elevation products; (iii) the characteristics of the morphodynamic drivers evaluated by adopting uncertainty-aware approaches. Thus, the contribution of subtle morphological changes—magnitudes comparable with the instrumental accuracy and/or the assessed propagated uncertainty—can be properly accounted for.
Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China

This paper presents a recent rainfall-induced landslide in China that occurred on August 21, 2020 and resulted in nine deaths. The sliding material traveled a distance of 800 m, with an altitude difference of about 180 m. A field investigation, remote sensing based on an unmanned aerial vehicle (UAV), in situ monitoring, and a rainfall data analysis were carried out to reveal the deposit characteristics, causative factors, post-landslide behavior, and the mechanism of the landslide. A saltatory micro-relief of the original slope determined the multiple-stage failure type of the slide, and also promoted the entrainment effect during the landslide movement. After the first-initiation sliding stage, the motion of this landslide involved typical progressive movement, and over time, the style of the runout generally turned into a flow-like form. Furthermore, the antecedent cumulative rainfall of 149.5 mm directly contributed to the occurrence of the landslide. Using the GB-SAR early warning system, the post-landslide residual failure was successfully predicted 10 min in advance. The combination of the UAV and GB-SAR technique can surely be beneficial for other inaccessible landslide investigations as well and improves the emergency rescue security.
Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.
RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.