Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain Access Control Ecosystem for Big Data Security

In recent years, the advancement in modern technologies has not only resulted in an explosion of huge data sets being captured and recorded in different fields, but also given rise to concerns in the security and protection of data during storage, transmission, processing, and access. The blockchain is a distributed ledger that records transactions in a secure, flexible, verifiable and permanent way. Transactions in a blockchain can be an exchange of an asset, the execution of the terms of a smart contract, or an update to a record. In this paper, we have developed a blockchain access control ecosystem that gives asset owners the sovereign right to effectively manage access control of large data sets and protect against data breaches. The Linux Foundation's Hyperledger Fabric blockchain is used to run the business network while the Hyperledger composer modeling tool is used to implement the smart contracts or transaction processing functions that run on the blockchain network.
Temperature Dependence of Mechanical, Electrical Properties and Crystal Structure of Polyethylene Blends for Cable Insulation

There is a long-standing puzzle concerning whether polyethylene blends are a suitable substitution for cable-insulation-used crosslinking polyethylene (XLPE) especially at elevated temperatures. In this paper, we investigate temperature dependence of mechanical, electrical properties of blends with 70 wt % linear low density polyethylene (LLDPE) and 30 wt % high density polyethylene (HDPE) (abbreviated as 70 L-30 H). Our results show that the dielectric loss of 70 L-30 H is about an order of magnitude lower than XLPE, and the AC breakdown strength is 22% higher than XLPE at 90 °C. Moreover, the dynamic mechanical thermal analysis (DMA) measurement and hot set tests suggest that the blends shows optimal mechanical properties especially at high temperature with considerable temperature stability. Further scanning electron microscope (SEM) observation and X-ray diffraction (XRD) analysis uncover the reason for the excellent high temperature performance and temperature stability, which can be ascribed to the uniform fine-spherulite structure in 70 L-30 H blends with high crystallinity sustaining at high temperature. Therefore, our findings may enable the potential application of the blends as cable insulation material with higher thermal-endurance ability.
Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.
Physiologically based pharmacokinetic modelling of acute digoxin toxicity and the effect of digoxin-specific antibody fragments

Abstract Context: Recommended doses of digoxin-specific antibody fragments (digoxin-Fab) for treatment of acute digoxin poisoning are pharmacokinetically unsubstantiated and theoretically excessive. Physiologically based pharmacokinetic (PBPK) modelling creates clinical simulations which are closely related to physiological and pharmacokinetic behaviour. This paper details the formulation of a PBPK model of digoxin and explores its use as a simulation tool for acute digoxin toxicity and its management. Materials and methods: A PBPK model of digoxin was constructed and validated for acute digoxin poisoning management by comparing simulations with observed individual acute overdose patients. These simulations were compared with standard two-compartment PK model simulations. Results: PBPK model simulations showed good agreement with post-absorption plasma concentrations of digoxin measured in 6 acute overdose patients. PBPK predictions were accurate to 1.5-fold or less of observed clinical values, proving to be more accurate than two-compartment simulations of the same patients which produced up to a 4.9-fold change. Conclusions: Compared to conventional two-compartment modelling, PBPK modelling is superior in generating realistic simulations of acute digoxin toxicity and the response to digoxin-Fab. Simulation capacity provides realistic, continuous data which has the potential to substantiate alternative, less expensive, and safer digoxin-Fab dosing strategies for the treatment of acute digoxin toxicity.
A 1,000-Year-Old RNA Virus

A virus related to plant chrysoviruses was found in numerous ancient samples of maize, with nearly complete genomes in three samples. The age of the ancient samples (i.e., about 1,000 years old) was confirmed by carbon dating. Chrysoviruses are persistent plant viruses. They infect their hosts from generation to generation by transmission through seeds and can remain in their hosts for very long time periods. When modern corn samples were analyzed, a closely related chrysovirus was found with only about 3% divergence from the ancient sequences. This virus represents the oldest known plant virus. ABSTRACT Only a few RNA viruses have been discovered from archaeological samples, the oldest dating from about 750 years ago. Using ancient maize cobs from Antelope house, Arizona, dating from ca. 1,000 CE, we discovered a novel plant virus with a double-stranded RNA genome. The virus is a member of the family Chrysoviridae that infect plants and fungi in a persistent manner. The extracted double-stranded RNA from 312 maize cobs was converted to cDNA, and sequences were determined using an Illumina HiSeq 2000. Assembled contigs from many samples showed similarity to Anthurium mosaic-associated virus and Persea americana chrysovirus, putative species in the Chrysovirus genus, and nearly complete genomes were found in three ancient maize samples. We named this new virus Zea mays chrysovirus 1. Using specific primers, we were able to recover sequences of a closely related virus from modern maize and obtained the nearly complete sequences of the three genomic RNAs. Comparing the nucleotide sequences of the three genomic RNAs of the modern and ancient viruses showed 98, 96.7, and 97.4% identities, respectively. Hence, in 1,000 years of maize cultivation, this virus has undergone about 3% divergence. IMPORTANCE A virus related to plant chrysoviruses was found in numerous ancient samples of maize, with nearly complete genomes in three samples. The age of the ancient samples (i.e., about 1,000 years old) was confirmed by carbon dating. Chrysoviruses are persistent plant viruses. They infect their hosts from generation to generation by transmission through seeds and can remain in their hosts for very long time periods. When modern corn samples were analyzed, a closely related chrysovirus was found with only about 3% divergence from the ancient sequences. This virus represents the oldest known plant virus.
Fatty acid biomarkers of dairy fat consumption and incidence of type 2 diabetes: A pooled analysis of prospective cohort studies

Background We aimed to investigate prospective associations of circulating or adipose tissue odd-chain fatty acids 15:0 and 17:0 and trans-palmitoleic acid, t16:1n-7, as potential biomarkers of dairy fat intake, with incident type 2 diabetes (T2D). Methods and findings Sixteen prospective cohorts from 12 countries (7 from the United States, 7 from Europe, 1 from Australia, 1 from Taiwan) performed new harmonised individual-level analysis for the prospective associations according to a standardised plan. In total, 63,682 participants with a broad range of baseline ages and BMIs and 15,180 incident cases of T2D over the average of 9 years of follow-up were evaluated. Study-specific results were pooled using inverse-variance–weighted meta-analysis. Prespecified interactions by age, sex, BMI, and race/ethnicity were explored in each cohort and were meta-analysed. Potential heterogeneity by cohort-specific characteristics (regions, lipid compartments used for fatty acid assays) was assessed with metaregression. After adjustment for potential confounders, including measures of adiposity (BMI, waist circumference) and lipogenesis (levels of palmitate, triglycerides), higher levels of 15:0, 17:0, and t16:1n-7 were associated with lower incidence of T2D. In the most adjusted model, the hazard ratio (95% CI) for incident T2D per cohort-specific 10th to 90th percentile range of 15:0 was 0.80 (0.73–0.87); of 17:0, 0.65 (0.59–0.72); of t16:1n7, 0.82 (0.70–0.96); and of their sum, 0.71 (0.63–0.79). In exploratory analyses, similar associations for 15:0, 17:0, and the sum of all three fatty acids were present in both genders but stronger in women than in men (pinteraction < 0.001). Whereas studying associations with biomarkers has several advantages, as limitations, the biomarkers do not distinguish between different food sources of dairy fat (e.g., cheese, yogurt, milk), and residual confounding by unmeasured or imprecisely measured confounders may exist. Conclusions In a large meta-analysis that pooled the findings from 16 prospective cohort studies, higher levels of 15:0, 17:0, and t16:1n-7 were associated with a lower risk of T2D.
Single Channel speech separation based on empirical mode decomposition and Hilbert Transform

In this study, the authors discuss unsupervised separation of two speakers from single microphone recording using empirical mode decomposition (EMD) and Hilbert transform (HT) generally known as Hilbert-Huang transform. A two-stage separation procedure is proposed for single-channel (SC) speech separation. Initial stage of separation is done using EMD, HT and instantaneous frequencies. EMD decomposes the mixed signal into oscillatory functions known as intrinsic mode functions (IMFs). Suitable IMFs are selected using successive EMD decomposition and HT is applied to extract the instantaneous frequencies. The speech frames are grouped into two speakers using correlation of instantaneous frequencies between mixed signal and selected IMFs. Second-stage separation is done by further decomposing the estimated speakers into IMFs and finding the instantaneous amplitudes using HT. A ratio of instantaneous amplitudes of mixed speech and stage 1 recovered speech signal is computed for both speakers. Histogram of the ratio obtained can be used to estimate the ideal binary mask for each speaker. These masks are applied to the speech mixture and the underlying speakers are estimated. The proposed method was compared with the existing unsupervised SC source separation algorithms. The results show significant improvement in objective measures.
Sequence Alterations of Cortical Genes Linked to Individual Connectivity of the Human Brain.

Individual differences in humans are driven by unique brain structural and functional profiles, presumably mediated in part through differential cortical gene expression. However, the relationships between cortical gene expression profiles and individual differences in large-scale neural network organization remain poorly understood. In this study, we aimed to investigate whether the magnitude of sequence alterations in regional cortical genes mapped onto brain areas with high degree of functional connectivity variability across individuals. First, human genetic expression data from the Allen Brain Atlas was used to identify protein-coding genes associated with cortical areas, which delineated the regional genetic signature of specific cortical areas based on sequence alteration profiles. Thereafter, we identified brain regions that manifested high degrees of individual variability by using test-retest functional connectivity magnetic resonance imaging and graph-theory analyses in healthy subjects. We found that rates of genetic sequence alterations shared a distinct spatial topography with cortical regions exhibiting individualized (highly-variable) connectivity profiles. Interestingly, gene expression profiles of brain regions with highly individualized connectivity patterns and elevated number of sequence alterations are devoted to neuropeptide-signaling-pathways and chemical-synaptic-transmission. Our findings support that genetic sequence alterations may underlie important aspects of brain connectome individualities in humans.Significance Statement: The neurobiological underpinnings of our individuality as humans are still an unsolved question. Although the notion that genetic variation drives an individual's brain organization has been previously postulated, specific links between neural connectivity and gene expression profiles have remained elusive. In this study, we identified the magnitude of population-based sequence alterations in discrete cortical regions and compared them to the brain topological distribution of functional connectivity variability across an independent human sample. We discovered that brain regions with high degree of connectional individuality are defined by increased rates of genetic sequence alterations; these findings specifically implicated genes involved in neuropeptide-signaling pathways and chemical-synaptic transmission. These observations support that genetic sequence alterations may underlie important aspects of the emergence of the brain individuality across humans.
A comparison of matched interim analysis publications and final analysis publications in oncology clinical trials

Background Progression-free survival is an increasingly popular surrogate end point for overall survival. The strength of correlation between the two end points varies, raising questions about the correlation between results of interim analyses that report mature progression-free survival data with the subsequent final publication that report overall survival. Methods We searched PubMed from 2005 to 2015 for randomized controlled trials that measured both progression-free survival and overall survival. We matched interim publications that reported mature progression-free survival data with their final analyses that reported overall survival. We included 25 matched pairs and 8 unmatched interim analyses whose final analyses are not published. Our primary objectives are to compare interim publications with matched final publications in terms of journal prominence and Altmetric score and to compare progression-free survival and overall survival effect sizes. Results All interim analyses (n = 33) were prespecified and there was a statistically significant progression-free survival benefit in 31 (93.9%). Only eight matched final analyses had statistically significant overall survival data. Interim analyses were more often published in top-5 general medicine journals (P < 0.01) but not in top-5 oncology journals (P = 0.26). Altmetric scores were higher in interim analyses (P < 0.01). Progression-free survival effect sizes from interim analyses were a median of 31% larger than overall survival effect sizes from final analyses. Conclusion Interim analyses with progression-free survival data may generate hype in oncology, as evidenced by journal impact factors and Altmetric scores. The cause of this hype may be due, in part, to large progression-free survival effect sizes. Regardless, in trials that investigate progression-free and overall survival, publishing interim analyses with mature progression-free survival data apart from the final analyses with mature overall survival should be cautioned.
Face, hairstyle and clothing colour de-identification in video sequences

The authors introduce a system for person de-identification in video data that de-identifies biometric and non-biometric features, namely faces, hairstyles and clothing colours. The authors’ system detects human faces and silhouettes in the input video and replaces the detected faces with random synthesised faces obtained using a deep convolutional generative adversarial network. Alternative hairstyles are rendered over the synthesised faces, and the human silhouette is recoloured so that skin hues are preserved and clothing hues are altered. Through the use of artificially synthesised faces that look realistic, they ensure that the de-identified image looks natural and at the same time avoid ethical and legal considerations present when using real face images as replacement faces. As they address non-biometric feature de-identification, their system offers a considerably higher level of privacy protection than commonly employed solutions that use simple image processing techniques such as blurring. Qualitative and quantitative evaluation suggests that their system produces de-identified images that look natural, at the same time being resistant to re-identification attacks.
Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing

Most of current studies on human gaze and saliency modeling have used high-quality stimuli. In real world, however, captured images undergo various types of distortions during the whole acquisition, transmission, and displaying chain. Some distortion types include motion blur, lighting variations and rotation. Despite few efforts, influences of ubiquitous distortions on visual attention and saliency models have not been systematically investigated. In this paper, we first create a large-scale database including eye movements of 10 observers over 1900 images degraded by 19 types of distortions. Second, by analyzing eye movements and saliency models, we find that: a) observers look at different locations over distorted versus original images, and b) performances of saliency models are drastically hindered over distorted images, with the maximum performance drop belonging to Rotation and Shearing distortions. Finally, we investigate the effectiveness of different distortions when serving as data augmentation transformations. Experimental results verify that some useful data augmentation transformations which preserve human gaze of reference images can improve deep saliency models against distortions, while some invalid transformations which severely change human gaze will degrade the performance.
Mutations of RAS/RAF Proto-oncogenes Impair Survival After Cytoreductive Surgery and HIPEC for Peritoneal Metastasis of Colorectal Origin

Background: Adequate selection of patients with peritoneal metastasis (PM) for cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) remains critical for successful long-term outcomes. Factors reflecting tumor biology are currently poorly represented in the selection process. The prognostic relevance of RAS/RAF mutations in patients with PM remains unclear. Methods: Survival data of patients with colorectal PM operated in 6 European tertiary centers were retrospectively collected and predictive factors for survival identified by Cox regression analyses. A simple point-based risk score was developed to allow patient selection and outcome prediction. Results: Data of 524 patients with a median age of 59 years and a median peritoneal cancer index of 7 (interquartile range: 3–12) were collected. A complete resection was possible in 505 patients; overall morbidity and 90-day mortality were 50.9% and 2.1%, respectively. PCI [hazard ratio (HR): 1.08], N1 stage (HR: 2.15), N2 stage (HR: 2.57), G3 stage (HR: 1.80) as well as KRAS (HR: 1.46) and BRAF (HR: 3.97) mutations were found to significantly impair survival after CRS/HIPEC on multivariate analyses. Mutations of RAS/RAF impaired survival independently of targeted treatment against EGFR. Consequently, a simple point-based risk score termed BIOSCOPE (BIOlogical Score of COlorectal PEritoneal metastasis) based on PCI, N-, G-, and RAS/RAF status was developed, which showed good discrimination [development area under the curve (AUC) = 0.72, validation AUC = 0.70], calibration (P = 0.401) and allowed categorization of patients into 4 groups with strongly divergent survival outcomes. Conclusion: RAS/RAF mutations impair survival after CRS/HIPEC. The novel BIOSCOPE score reflects tumor biology, adequately stratifies long-term outcomes, and improves patient assessment and selection.
Social Media Analyst Responding Tool: A Visual Analytics Prototype to Identify Relevant Tweets in Emergency Events

Public and humanitarian organizations monitor social media to extract useful information during emergencies. In this paper, we propose a new method for identifying situation awareness (SA) tweets for emergencies. We take a human centered design approach to developing a visual analytics prototype, SMA-RT (“Social Media Analyst Responding Tool”), informed by social media analysts and emergency practitioners. Our design offers insights into the main requirements of social media monitoring tools used for emergency purposes. It also highlights the role that human and technology can play together in such solutions. We embed a machine learning classifier to identify SA tweets in a visual interactive tool. Our classifier aggregates textual, social, location, and tone based features to increase precision and recall of SA tweets.
Learning Context For Semantic Segmentation and Applications

In this work, we present two semantic segmentation methods designed to automatically learn local and global context relations from data. Our methods are able to handle both 2D and 3D information. We also introduce a framework for generation of synthetic ground truth data. Furthermore, we present two automotive applications build on top of our semantic segmentation method: parking space detection and traffic light detection.
Whole Genome Characterization of Orthopoxvirus (OPV) Abatino, a Zoonotic Virus Representing a Putative Novel Clade of Old World Orthopoxviruses

Orthopoxviruses (OPVs) are diffused over the complete Eurasian continent, but previously described strains are mostly from northern Europe, and few infections have been reported from Italy. Here we present the extended genomic characterization of OPV Abatino, a novel OPV isolated in Italy from an infected Tonkean macaque, with zoonotic potential. Phylogenetic analysis based on 102 conserved OPV genes (core gene set) showed that OPV Abatino is most closely related to the Ectromelia virus species (ECTV), although placed on a separate branch of the phylogenetic tree, bringing substantial support to the hypothesis that this strain may be part of a novel OPV clade. Extending the analysis to the entire set of genes (coding sequences, CDS) further substantiated this hypothesis. In fact the genome of OPV Abatino included more CDS than ECTV; most of the extra genes (mainly located in the terminal genome regions), showed the highest similarity with cowpox virus (CPXV); however vaccinia virus (VACV) and monkeypox virus (MPXV) were the closest OPV for certain CDS. These findings suggest that OPV Abatino could be the result of complex evolutionary events, diverging from any other previously described OPV, and may indicate that previously reported cases in Italy could represent the tip of the iceberg yet to be explored.