Miracle-dz/UniFAR-FTUs huggingface dataset:
Alterations of the Fecal Microbiota in Chinese Patients With Multiple Sclerosis

Mounting evidence indicates that alterations in the intestinal microbiota may be associated with neurological disorders such as multiple sclerosis (MS). MS is a putative autoimmune disease of the central nervous system. However, it has not been determined whether the intestinal microbiota and host immune status are altered in Chinese patients with stable MS. In our study, 22 Chinese patients with stable MS and 33 healthy controls were enrolled for fecal microbiota analysis and host immunity evaluation. The microbial diversity and composition, bacterial co-occurrence correlations, predictive functional profiles, and microbiota-cytokine correlations between the two groups were compared. We observed that while the overall structure of the fecal microbiota did not change significantly, the abundances of several key functional bacteria, primarily Faecalibacterium, decreased remarkably. Faecalibacterium and Granulicatella could be used to distinguish between patients with MS and healthy controls with an area under the curve of 0.832. PiCRUSt analysis revealed that genes associated with fructose, mannose, and fatty acid metabolism were significantly enriched in the MS microbiota. In addition, we also observed that the levels of several pro- and anti-inflammatory cytokines and chemokines, such as IL-1ra, IL-8, IL-17, and TNF-α changed observably, and the abundances of key functional bacteria like butyrate producers correlated with the changes in the cytokine levels. Our present study indicated that altered composition of the fecal microbiota might play vital roles in the etiopathogenesis of MS by regulating host immunity, which suggests that microbiota-targeting patient-tailored early intervention techniques might serve as novel therapeutic approaches for MS.
Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

The application of machine learning for the development of clinical decision-support systems in audiology provides the potential to improve the objectivity and precision of clinical experts' diagnostic decisions. However, for successful clinical application, such a tool needs to be accurate, as well as accepted and trusted by physicians. In the field of audiology, large amounts of patients' data are being measured, but these are distributed over local clinical databases and are heterogeneous with respect to the applied assessment tools. For the purpose of integrating across different databases, the Common Audiological Functional Parameters (CAFPAs) were recently established as abstract representations of the contained audiological information describing relevant functional aspects of the human auditory system. As an intermediate layer in a clinical decision-support system for audiology, the CAFPAs aim at maintaining interpretability to the potential users. Thus far, the CAFPAs were derived by experts from audiological measures. For designing a clinical decision-support system, in a next step the CAFPAs need to be automatically derived from available data of individual patients. Therefore, the present study aims at predicting the expert generated CAFPA labels using three different machine learning models, namely the lasso regression, elastic nets, and random forests. Furthermore, the importance of different audiological measures for the prediction of specific CAFPAs is examined and interpreted. The trained models are then used to predict CAFPAs for unlabeled data not seen by experts. Prediction of unlabeled cases is evaluated by means of model-based clustering methods. Results indicate an adequate prediction of the ten distinct CAFPAs. All models perform comparably and turn out to be suitable choices for the prediction of CAFPAs. They also generalize well to unlabeled data. Additionally, the extracted relevant features are plausible for the respective CAFPAs, facilitating interpretability of the predictions. Based on the trained models, a prototype of a clinical decision-support system in audiology can be implemented and extended towards clinical databases in the future.
Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.
Turbidity Estimation from GOCI Satellite Data in the Turbid Estuaries of China's Coast

Knowledge of the distribution and variation of water turbidity directly represent important information related to the marine ecology and multiple biogeochemical processes, including sediment transport and resuspension and heat transfer in the upper water layer. In this study, a neural network (NN) approach was applied to derive the water turbidity using the geostationary ocean color imager (GOCI) data in turbid estuaries of the Yellow River and the Yangtze River. The results showed a good agreement between the GOCI-derived turbidity and in situ measured data with a determination coefficient (R2) of 0.84, root mean squared error (RMSE) of 58.8 nephelometric turbidity unit (NTU), mean absolute error of 25.1 NTU, and mean relative error of 34.4%, showing a better performance than existing empirical algorithms. The hourly spatial distributions of water turbidity in April 2018 suggested that high turbidity regions were distributed in the Yellow River estuary, Yangtze River estuary, Hangzhou Bay, and coastal waters of Zhejiang Province. Furthermore, the relationship between water turbidity and tide were estimated. A defined turbid zone was defined to evaluate the diurnal variations of turbidity, which has subtle changes at different times. Our results showed an inverse relationship between turbidity and tide over six selected stations, i.e., when the value of turbidity is high, then the corresponding tidal height is usually low, and vice versa. The combined effects of tidal height and tidal currents could explain the phenomena, and other factors such as winds also contribute to the turbidity distributions.
Exploring the Variability of Tropical Savanna Tree Structural Allometry with Terrestrial Laser Scanning

Individual tree carbon stock estimates typically rely on allometric scaling relationships established between field-measured stem diameter (DBH) and destructively harvested biomass. The use of DBH-based allometric equations to estimate the carbon stored over larger areas therefore, assumes that tree architecture, including branching and crown structures, are consistent for a given DBH, and that minor variations cancel out at the plot scale. We aimed to explore the degree of structural variation present at the individual tree level across a range of size-classes. We used terrestrial laser scanning (TLS) to measure the 3D structure of each tree in a 1 ha savanna plot, with coincident field-inventory. We found that stem reconstructions from TLS captured both the spatial distribution pattern and the DBH of individual trees with high confidence when compared with manual measurements (R2 = 0.98, RMSE = 0.0102 m). Our exploration of the relationship between DBH, crown size and tree height revealed significant variability in savanna tree crown structure (measured as crown area). These findings question the reliability of DBH-based allometric equations for adequately representing diversity in tree architecture, and therefore carbon storage, in tropical savannas. However, adoption of TLS outside environmental research has been slow due to considerable capital cost and monitoring programs often continue to rely on sub-plot monitoring and traditional allometric equations. A central aspect of our study explores the utility of a lower-cost TLS system not generally used for vegetation surveys. We discuss the potential benefits of alternative TLS-based approaches, such as explicit modelling of tree structure or voxel-based analyses, to capture the diverse 3D structures of savanna trees. Our research highlights structural heterogeneity as a source of uncertainty in savanna tree carbon estimates and demonstrates the potential for greater inclusion of cost-effective TLS technology in national monitoring programs.
Spatio-Temporal Dynamics of Algae and Macrophyte Cover in Urban Lakes: A Remote Sensing Analysis of Bellandur and Varthur Wetlands in Bengaluru, India

Rapid urbanization processes and indiscriminate disposal of urban wastewaters are major causes for anthropogenic lake-sediment deposition and eutrophication. However, information about the spatial and temporal variation of macrophyte and phytoplankton distribution as indicators for water contamination is limited. To gain insights into the dynamics, we analyzed lake-cover changes of Bellandur and Varthur Lake in the S-Indian megacity of Bengaluru for the post-rainy seasons of the years 2002–2019. Supervised maximum likelihood classifications were conducted on 62 freely available, true-color satellite images in order to distinguish between macrophytes, algae, and free water surface. The image-derived results were verified by supervised classification and manual mapping of two simultaneously recorded multispectral satellite images (Sentinel-2 and WorldView-2). Seasonal interrelations between macrophytes and algae distribution were similar for both lakes. The increase in macrophyte cover during post-rainy season negatively correlated with algal abundance. Macrophyte expansion progressively suppressed algae development at both lakes, reflective of increasing eutrophication caused by on-going wastewater input. Seasonal variation in precipitation, wind direction, and temperature seemed to trigger intra-annual shifts of macrophytes and algae while similar macrophyte spread intensities during the post-monsoon season indicated independence of nutrient loads in the lake water.
Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.
Towards a reference process for software architecture reconstruction

Nowadays, software systems remain useful and competitive; entail the inevitable need to change over time and to be adapted to new technologies, platforms, and architectures. These quick changes imply following systematic, automated, or standardised processes that provide recommendations and guidelines to architects during software architecture reconstruction. Considerable research work on architecture reconstruction has been conducted. However, it needs to be studied thoroughly to determine what are the common activities and elements to reconstruct software architectures, and to define a reference process for systematically guiding the evolution of software architectures. This work addresses the need for defining a process for software architecture reconstruction called software improvement in the reconstruction of architectures (SIRA). This process has been rigorously designed from the results of a systematic literature review and a small survey of related work. As a result, SIRA integrates and extends previous research and can be conceived as a reference process to reconstruct software architectures in a semi-automated way. In addition, this work also determines the common elements of the architecture reconstruction process: (i) techniques and activities; (ii) architectural elements, patterns, and attributes; (iii) mechanisms and strategies; and (iv) the automation and recommendation tasks of the process.
Modeling Bidirectional Polarization Distribution Function of Land Surfaces Using Machine Learning Techniques

Accurate estimation of polarized reflectance (Rp) of land surfaces is critical for remote sensing of aerosol optical properties. In the last two decades, many data-driven bidirectional polarization distribution function (BPDF) models have been proposed for accurate estimation of Rp, among which the generalized regression neural network (GRNN) based BPDF model has been reported to perform the best. GRNN is just a simple machine learning (ML) technique that can solve non-linear problems. Many ML techniques were reported to work well in solving non-linear problems and consequently may provide better performance in BPDF modeling. However, incorporating various ML techniques with BPDF modeling and comparing their performances have never been well documented. In this study, three widely used ML algorithms—i.e., support vector regression (SVR), K-nearest-neighbor (KNN), and random forest (RF)—were applied for BPDF modeling. Using measurements collected by the Polarization and Directionality of the Earth’s Reflectance onboard PARASOL satellite (POLDER/PARASOL), non-linear relationships between Rp and the input variables, i.e., Fresnel factor (Fp), scattering angle (SA), reflectance at 670 nm (R670) and 865 nm (R865), were built using these ML algorithms. Results showed that taking Fp, SA, R670, and R865 as input variables, the performance of the four ML-based BPDF models was quite similar. The KNN-based BPDF model provided slightly better results, and improved the accuracy of the semi-empirical BPDF models by 9.55% in terms of the overall root mean square error (RMSE). Experiments of different configuration of input variables suggested that using multi-band reflectance as input variables provided better results than using vegetation indices. The RF-based BPDF model using all reflectances at six bands as input variables produced the best results, improving the overall accuracy by 6.62% compared with the GRNN-based BPDF model. Among all the input variables, reflectance at absorbing spectral bands—e.g., 490 nm and 670 nm—played more significant roles in RF-based BPDF modeling due to the domination of polarized partition in total reflectance. Fresnel factor and scattering angle were also important for BPDF modeling. This study confirmed the feasibility of applying ML techniques to more accurate BPDF modeling, and the RF-based BPDF model proposed in this study can be used to increase the accuracy of remote sensing of the complete aerosol properties.
Diversified Fisher kernel: encoding discrimination in Fisher features to compete deep neural models for visual classification task

: Fisher kernels derived from stochastic probabilistic models such as restricted and deep Boltzmann machines have shown competitive visual classification results in comparison to widely popular deep discriminative models. This genre of Fisher kernels bridges the gap between shallow and deep learning paradigm by inducing the characteristics of deep architecture into Fisher kernel, further deployed for classification in discriminative classifiers. Despite their success, the memory and computational costs of Fisher vectors do not make them amenable for large-scale visual retrieval and classification tasks. This study introduces a novel feature selection technique inspired from the functional characteristics of neural architectures for learning discriminative feature representations to boost the performance of Fisher kernels against deep discriminative models. The proposed technique condenses the large dimensional Fisher features for kernel learning and shows improvement in its classification performance and storage cost on leading benchmark data sets. A comparison of the proposed method with other state-of-the-art feature selection techniques is made to demonstrate its performance supremacy as well as time complexity required to learn in reduced Fisher space.
What (if any) behaviour change techniques do government-led cybersecurity awareness campaigns use?

With the surge in cyber incidents in recent years, many linked to human error, governments are quite naturally developing security campaigns to improve citizens’ security behaviour. However, it remains not only unclear how successful these campaigns are in changing behaviour, but also what established behaviour change techniques—if any—they employ in order to achieve this goal. To investigate this, we analysed 17 government-sponsored cybersecurity campaign materials. We coded the materials for their intervention functions according to the Behaviour Change Wheel and their behaviour change techniques in accordance with the Behavioural Change Technique Taxonomy (version 1). Our findings show that security campaigns are often focused on education and increasing awareness, under the assumption that as long as citizens are aware of the risk, and are provided with information on how to improve their security behaviour, behaviour will change. Additionally, there is a lack of published effectiveness studies investigating the direct effects of a governmental cybersecurity campaign. Proposed improvements to security campaigns are discussed.
Impact of antenna correlation on the physical layer security of cooperative relaying with OSTBC system

: This study presents an analysis for evaluating the physical layer (PHY) security performance of a two-hop amplify-and-forward cooperative relaying system employing Alamouti orthogonal space–time block coding (OSTBC) in the presence of an eavesdropper, where the transmit antennas for OSTBC are assumed to be spatially correlated. It is also assumed that the authorised main channels as well as the non-authorised eavesdropper channels follow Rayleigh fading distribution. Closed-form mathematical expressions are derived for three performance metrics for evaluating the PHY security, namely the probability of non-zero secrecy capacity, the secrecy outage probability, and the average secrecy capacity. Using the extensive numerical results obtained from the derived mathematical expressions, the impact of antennas correlation on the PHY secrecy performance of the system is studied and evaluated under different parameters. It is also shown that the antennas correlation corresponding to the main channels has more impact than the antennas correlation corresponding to the eavesdropper channels.
Spatial and Temporal Human Settlement Growth Differentiation with Symbolic Machine Learning for Verifying Spatial Policy Targets: Assiut Governorate, Egypt as a Case Study

Since 2005, Egypt has a new land-use development policy to control unplanned human settlement growth and prevent outlying growth. This study assesses the impact of this policy shift on settlement growth in Assiut Governorate, Egypt, between 1999 and 2020. With symbolic machine learning, we extract built-up areas from Landsat images of 2005, 2010, 2015, and 2020 and a Landscape Expansion Index with a new QGIS plugin tool (Growth Classifier) developed to classify settlement growth types. The base year, 1999, was produced by the national remote sensing agency. After extracting the built-up areas from the Landsat images, eight settlement growth types (infill, expansion, edge-ribbon, linear branch, isolated cluster, proximate cluster, isolated scattered, and proximate scattered) were identified for four periods (1999:2005, 2005:2010, 2010:2015, and 2015:2020). The results show that prior to the policy shift of 2005, the growth rate for 1999–2005 was 11% p.a. In all subsequent periods, the growth rate exceeded the target rate of 1% p.a. , though by varying amounts. The observed settlement growth rates were 5% (2005:2010), 7.4% (2010:2015), and 5.3% (2015:2020). Although the settlements in Assiut grew primarily through expansion and infill, with the latter growing in importance during the last two later periods, outlying growth is also evident. Using four class metrics (number of patches, patch density, mean patch area, and largest patch index) for the eight growth types, all types showed a fluctuated trend between all periods, except for expansion, which always tends to increase. To date, the policy to control human settlement expansion and outlying growth has been unsuccessful.