Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods

As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role.However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks.Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks.We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization this http URL introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail.The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88.7% and 74.1% on average in different ML-based detection methods.
A Fast and Accurate Index Structure for Spatiotemporal Trajectories

There are many applications that rely on the analysis of historical trajectory data provided by location-aware devices. Traffic management, ridesharing and Pay-As-You-Drive insurance are some example applications. These applications require one to scan a large-scale historical dataset in order to extract similar trajectories to a query trajectory. A naive approach is a one-by-one similarity comparison between the query trajectory and dataset trajectories. However, most existing methods for assessing similarity between pairs of trajectories have quadratic time-complexity, making sequential scan of a large dataset computationally expensive. Existing solutions for this problem mainly focus on reducing the computation time by summarizing the trajectories or by defining upper-bounds (lower-bounds) for the similarity between two trajectories. However, these solutions do not directly address the efficiency challenge. To tackle this challenge of large scale similar trajectory retrieval, we propose an efficient index structure that filters out distant trajectories and outputs an approximate set of similar trajectories. Experimental results show that our novel index structure significantly improves the efficiency of trajectory retrieval, whilst maintaining high accuracy.
Correlation-aware semi-analytic visibility for antialiased rendering

Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8x, analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256x MSAA, faster than 64x MSAA, and constant space per pixel.
Label-free Molecular Imaging and Analysis by Raman Spectroscopy

Raman scattering of a cell conveys the intrinsic information inherent to chemical structures of biomolecules. The spectroscopy of Raman scattering, or Raman spectroscopy, allows label-free and quantitative molecular sensing of a biological sample in situ without disruption. For the last five decades Raman spectroscopy has been widely utilized in biological research fields. However, it is just within the latest decade that molecular imaging and discrimination of living cells and tissues have become practically available. Here we overview recent progress in Raman spectroscopy and its application to life sciences. We discuss imaging of functional molecules in living cells and tissues; e.g., cancer cells and ischemic or infarcted hearts, together with a number of studies in the biomedical fields. We further explore comprehensive understandings of a complex spectrum by multivariate analysis for, e.g., accurate peripheral nerve detection, and characterization of the histological differences in the healing process of myocardial infarct. Although limitations still remain, e.g., weakness of the scattering intensity and practical difficulty in comprehensive molecular analysis, continuous progress in related technologies will allow wider use of Raman spectroscopy for biomedical applications.
Psychometric properties of the Chinese version of the Stress‐Related Growth Scale: Short Form in undergraduate nursing students

In this study, we examined the psychometric properties of the Chinese version of the Stress-Related Growth Scale-Short Form (SRGS-SF) for undergraduate nursing students. Using a cross-sectional design, a convenience sample of 253 undergraduate nursing students was included in this study. Half of the sample (n = 126) was randomly selected to explore the factorial structure of the SRGS-SF via exploratory factor analysis. The rest of the sample (n = 127) was selected to confirm the structure via confirmatory factor analysis. The results showed that the Chinese version of the SRGS-SF consisted of two factors: interpersonal and intrapersonal growth. The scale also had the power to discriminate between undergraduate nursing students with high and low levels of stress-related growth (SRG). The internal consistency and stability of the scale were also adequate. The Chinese version of the SRGS-SF is a valid and reliable measurement for assessing SRG among undergraduate nursing students. One suitable application would be the evaluation of the process underpinning SRG and the effectiveness of interventions.
MNPR: a framework for real-time expressive non-photorealistic rendering of 3D computer graphics

We propose a framework for expressive non-photorealistic rendering of 3D computer graphics: MNPR. Our work focuses on enabling stylization pipelines with a wide range of control, thereby covering the interaction spectrum with real-time feedback. In addition, we introduce control semantics that allow cross-stylistic art-direction, which is demonstrated through our implemented watercolor, oil and charcoal stylizations. Our generalized control semantics and their style-specific mappings are designed to be extrapolated to other styles, by adhering to the same control scheme. We then share our implementation details by breaking down our framework and elaborating on its inner workings. Finally, we evaluate the usefulness of each level of control through a user study involving 20 experienced artists and engineers in the industry, who have collectively spent over 245 hours using our system. MNPR is implemented in Autodesk® Maya® and open-sourced through this publication, to facilitate adoption by artists and further development by the expressive research and development community.
Nonlinear Disturbance Attenuation Control of Hydraulic Robotics

This paper presents a novel nonlinear disturbance rejection control for hydraulic robots. This method requires two third-order filters as well as inverse dynamics in order to estimate the disturbances. All the parameters for the third-order filters are pre-defined. The proposed method is nonlinear, which does not require the linearization of the rigid body dynamics. The estimated disturbances are used by the nonlinear controller in order to achieve disturbance attenuation. The performance of the proposed approach is compared with existing approaches. Finally, the tracking performance and robustness of the proposed approach is validated extensively on real hardware by performing different tasks under either internal or both internal and external disturbances. The experimental results demonstrate the robustness and superior tracking performance of the proposed approach.
Association between Obesity and Circulating Brain-Derived Neurotrophic Factor (BDNF) Levels: Systematic Review of Literature and Meta-Analysis

Reduction in brain-derived neurotrophic factor (BDNF) expression in the brain as well as mutations in BDNF gene and/or of its receptor are associated to obesity in both human and animal models. However, the association between circulating levels of BDNF and obesity is still not defined. To answer this question, we performed a meta-analysis carrying out a systematic search in electronic databases. Ten studies (307 obese patients and 236 controls) were included in the analysis. Our data show that obese patients have levels of BDNF similar to those of controls (SMD: 0.01, 95% CI: −0.28, 0.30, p = 0.94). The lack of difference was further confirmed both in studies in which BDNF levels were assessed in serum (MD: −0.93 ng/mL, 95% CI: −3.34, 1.48, p = 0.45) and in plasma (MD: 0.15 ng/mL, 95% CI: −0.09, 0.39, p = 0.23). Data evaluation has shown that some bias might affect BDNF measurements (e.g., subject recruitment, procedures of sampling, handling, and storage), leading to a difficult interpretation of the results. Standardization of the procedures is still needed to reach strong, affordable, and reliable conclusions.
Clinical and Molecular Correlates of Escherichia coli Bloodstream Infection from Two Geographically Diverse Centers in Rochester, Minnesota, and Singapore

Escherichia coli bacteremia is caused mainly by sequence type complex 131 (STc131) and two clades within its fluoroquinolone-resistance-associated H30 subclone, H30R1 and H30Rx. We examined clinical and molecular correlates of E. coli bacteremia in two geographically distinct centers. ABSTRACT Escherichia coli bacteremia is caused mainly by sequence type complex 131 (STc131) and two clades within its fluoroquinolone-resistance-associated H30 subclone, H30R1 and H30Rx. We examined clinical and molecular correlates of E. coli bacteremia in two geographically distinct centers. We retrospectively studied 251 unique E. coli bloodstream isolates from 246 patients (48 from the Mayo Clinic, Rochester, MN [MN], and 198 from Tan Tock Seng Hospital, Singapore [SG]), from October 2013 through March 2014. Isolates underwent PCR for phylogroup, STc, blaCTX-M type, and virulence gene profiles, and medical records were reviewed. Although STc131 accounted for 25 to 27% of all E. coli bacteremia isolates at each site, its extended-spectrum-β-lactamase (ESBL)-associated H30Rx clade was more prominent in SG than in MN (15% versus 4%; P = 0.04). In SG only, patients with STc131 (versus other E. coli STc isolates) were more likely to receive inactive initial antibiotics (odds ratio, 2.8; P = 0.005); this was true specifically for patients with H30Rx (odds ratio, 7.0; P = 0.005). H30Rx comprised 16% of community-onset bacteremia episodes in SG but none in MN. In SG, virulence scores were higher for H30Rx than for H30R1, non-H30 STc131, and non-STc131 isolates (P < 0.02 for all comparisons). At neither site did mortality differ by clonal status. The ESBL-associated H30Rx clade was more prevalent and more often of community onset in SG, where it predicted inactive empirical treatment. The clonal distribution varies geographically and has potentially important clinical implications. Rapid susceptibility testing and clonal diagnostics for H30/H30Rx might facilitate earlier prescribing of active therapy.
It Can be Cheaper: Using Price Prediction to Obtain Better Prices from Dynamic Pricing in Ride-on-demand Services

In emerging ride-on-demand (RoD) services such as Uber or Didi (in China), dynamic pricing plays an important role in regulating supply and demand, trying to make such service, to some extent, more convenient for passengers. Despite the convenience, dynamic pricing also exerts mental burden on passengers: they wonder whether the current price is low enough to accept, or if it is not, what they could do to get a lower price. Without extra information, passengers sometimes feel anxious and lose satisfaction. It is thus necessary to provide more information to relieve the anxiety, and price prediction is one of the solutions. In this paper we predict the dynamic prices to help passengers understand whether they could get a lower price in neighboring locations or within a short time. We first divide a city into rectangular cells, and use entropy and the temporal correlation of prices to characterize the predictability of prices of each cell. Based on the predictability of prices, we claim that different prediction algorithms should be used in different city areas, to balance between efficiency and accuracy. We design and implement two predictors, namely a Markov predictor and a neural network predictor, and evaluate their performance based on the real data we collected from a major RoD service provider in China. The results verify that the Markov predictor works well enough in highly-predictable areas, and the neural network predictor, while requiring more computation time, works better in areas with lower predictabilities. Finally, we also evaluate the effects of our prediction, i.e., the probability that passengers (in different city areas) could benefit from the prediction and get a lower price.
Detecting Falls Using a Wearable Accelerometer Motion Sensor

This research aims to early detect falls based on the rapid acceleration changes using the threshold based approach, using a single accelerometer. We propose the Acceleration Change-based Falls Detection Algorithm (ACFDA). The ACFDA observes and detects the rapid change of acceleration in vertical axis and the average value of signal magnitude vector of acceleration to differentiate falls from other activities of daily life (ADL). Initial results demonstrates that our algorithm achieved 100% of sensitivity, 95.65% of specificity and 96.35% of accuracy when tested with a total of 44 intentional falls and 230 ADLs in 32 datasets. Future work will focus on developing other strategies to reduce false alarms for improving both specificity and accuracy of the algorithm while still maintaining 100% of sensitivity.
Synthesis of Gold Functionalised Nanoparticles with the Eranthis hyemalis Lectin and Preliminary Toxicological Studies on Caenorhabditis elegans

The lectin found in the tubers of the Winter Aconite (Eranthis hyemalis) plant (EHL) is a Type II Ribosome Inactivating Protein (RIP). Type II RIPs have shown anti-cancer properties and have great potential as therapeutic agents. Similarly, colloidal gold nanoparticles are successfully used in biomedical applications as they can be functionalised with ligands with high affinity and specificity for target cells to create therapeutic and imaging agents. Here we present the synthesis and characterization of gold nanoparticles conjugated with EHL and the results of a set of initial assays to establish whether the biological effect of EHL is altered by the conjugation. Gold nanoparticles functionalised with EHL (AuNPs@EHL) were successfully synthesised by bioconjugation with citrate gold nanoparticles (AuNPs@Citrate). The conjugates were analysed by UV-Vis spectroscopy, Dynamic Light Scattering (DLS), Zeta Potential analysis, and Transmission Electron Microscopy (TEM). Results indicate that an optimal functionalisation was achieved with the addition of 100 µL of EHL (concentration 1090 ± 40 µg/mL) over 5 mL of AuNPs (concentration [Au0] = 0.8 mM). Biological assays on the effect of AuNPs@EHL were undertaken on Caenorhabditis elegans, a free-living nematode commonly used for toxicological studies, that has previously been shown to be strongly affected by EHL. Citrate gold nanoparticles did not have any obvious effect on the nematodes. For first larval stage (L1) nematodes, AuNPs@EHL showed a lower biological effect than EHL. For L4 stage, pre-adult nematodes, both EHL alone and AuNPs@EHL delayed the onset of reproduction and reduced fecundity. These assays indicate that EHL can be conjugated to gold nanoparticles and retain elements of biocidal activity.
Phase Formation of Mg-Zn-Gd Alloys on the Mg-rich Corner

The phase constitutions of as-cast magnesium (Mg)-Zn-Gd alloys (Zn/Gd = 0.25~60, Zn 0~10 at.%, Gd 0~2 at.%, 48 samples) were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The Mg-Zn-Gd phase diagram focused on the Mg-rich corner (with up to 20 at.% Zn, 10 at.% Gd) has been set up. Five regions can be classified as follows: (I) α-Mg+W-phase+(binary compounds), (II) α-Mg+W-phase+I-phase(+binary compounds), (III) α-Mg+I-phase(+binary compounds), (IV) α-Mg+binary compounds, and (V) α-Mg. The phase diagram has been verified by solidification behaviors observation using differential thermal analysis (DTA). Moreover, the structures of I-phase and W-phase in the alloy were explored in details. In terms of the Hume-Rothery rules, I-phase is confirmed as FK-type quasicrystalline with a chemical stoichiometry as Mg30±1Zn62Gd8±1 (at.%). The composition and lattice parameter a W-phase (fcc structure, m3¯m) are affected by the composition of Mg-Zn-Gd alloys, especially by the Zn/Gd ratio of alloys. This work would be instructive for the design of Mg-Zn-Gd alloys to obtain the phase components, and then selected the strengthening ways, which could adjust its mechanical properties.
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

Abstract The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins

Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL.
The Potential Protective Effect of Oligoribonucleotides-d-Mannitol Complexes against Thioacetamide-Induced Hepatotoxicity in Mice

This study investigated the potential hepatoprotective effect of oligoribonucleotides-d-mannitol complexes (ORNs-d-M) against thioacetamide (TAA)-induced hepatotoxicity in mice. The hepatoprotective activity of ORNs-d-M was evaluated in thioacetamide (TAA)-treated C57BL/6J. Results indicate that treatment with ORNs-d-M displayed a protective effect at the TAA-induced liver injury. Treatment with ORNs-d-M, starting at 0 h after the administration of TAA, decreased TAA-elevated serum alanine aminotransferase (ALT) and γ-glutamyl transpeptidase (GGT). Activities of glutathione S-transferase (GST) and glutathione peroxidase (GPx), and levels of glutathione (GSH), were enhanced with ORNs-d-M administration, while the hepatic oxidative biomarkers (TBA-reactive substances, protein carbonyl derivatives, protein-SH group) and myeloperoxidase (MPO) activity were reduced. Furthermore, genetic analysis has shown that the ORNs-d-M decreases the expression of mRNA pro-inflammatory cytokines, such as tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6), profibrogenic cytokine-transforming growth factor β1 (TGF-β1), as well as the principal protein of the extracellular matrix—collagen I. The present study demonstrates that ORNs-d-M exerts a protective effect against TAA-induced liver injury, which may be associated with its anti-inflammatory effects, inhibition of overexpression of mRNA cytokines, and direct effects on the metabolism of the toxin.