Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 and Phishing: Effects of Human Emotions, Behavior, and Demographics on the Success of Phishing Attempts During the Pandemic

Phishing is an online scam where criminals trick users with various strategies, with the goal of obtaining sensitive information or compromising accounts, systems, and/or other personal or organisational Information Technology resources. Multiple studies have shown that human factors influence the success of phishing attempts. However, these studies were conducted before the COVID-19 pandemic, which is significant because security reports show that the numbers of phishing attacks have been rapidly increasing since the start of COVID-19. This study investigates the extent to which users’ fear, anxiety and stress levels regarding COVID-19, impact falling for common and COVID-19 themed phishing scams during the outbreak period. Prior studies have depicted the effects of human behaviour on phishing attacks before the pandemic, such as risk-taking preferences and users’ demographic factors, hence this study also focuses on the effects of those factors on the likelihood of phishing victimisation. More concretely, we present the results of a scenario-based roleplay experiment to study the relationship between fear, anxiety, stress, risk-taking, and demographic factors and the success of phishing attacks during the pandemic. The findings indicate that fear of COVID-19 influences the success of COVID-19 specific themed phishing scams, while anxiety, stress, and risk-taking influences the success of both the COVID-19 themed and common phishing scams. Our findings also suggest that the users’ education level impacts common phishing attacks during the pandemic.
Plant stress activated by chlorine from disinfectants prepared on the base of sodium hypochlorite

Abstract In this study, the phytotoxicity of disinfectants prepared on the base of sodium hypochlorite was determined. For our tests two commercial products, Savo and Dom Amor, as well as 10% NaClO solution were used. While Savo contained only NaClO, Dom Amor contained NaClO and earthworm enzymes. Products on the base of NaClO are used in households for cleaning and disinfection of floors, furniture, sanitary and kitchen equipment. Savo may be used for the disinfection of drinking waters as well. Products with NaClO are also used for bacteria, algae and pathogens reduction in irrigation waters. As a subject, young seedlings of mustard Sinapis alba L. were used for the study of chronic toxicity. The observed parameters of the inhibition of roots and shoots growth, dry (DM) and fresh (FM) mass as well as photosynthetic pigments production (chlorophyll a, b, carotenoids) and water content in the plants were determined. The results point out that Dom Amor was the most toxic for S. alba seedlings growth and the rank order of the FAC contents for both plant parts was arranged as: Dom Amor > Savo > NaClO. All disinfectants reduced the DM and FM of roots; however, they stimulated biomass production in the shoots. On the basis of the obtained results it could be concluded, that disinfectants stimulated photosynthetic pigments production and reduced water content mainly in the roots. Dom Amor did not significantly reduced the water content in the shoots and for this parameter the following rank orders of inhibition for roots and shoots could be arranged as NaClO > Dom Amor > Savo and NaClO > Savo > Dom Amor, respectively. All commercial products increased chlorophyll a (Chla) and the carotenoids (Car) content in the shoots. As significant increase was confirmed first for Chla whose content in the presence of NaClO at concentration 24 mL/L overextended that in the control by 3.5 times. The rank orders of stimulation for Chla and Car were NaClO > Savo > Dom Amor and Dom Amor > NaClO > Savo, respectively.
Cyclic Prefix Noise Reduction for Digital Self Interference Cancellation in OFDM-Based In-Band Full-Duplex Wireless Systems

Orthogonal frequency division multiplexing (OFDM) is the pertinent waveform for current and next generation wireless systems, whose spectral efficiency can be potentially doubled by in-band full-duplex (IBFD) communication. However, in OFDM based IBFD systems, digital self-interference cancellation (DSIC) employed at baseband does not provide sufficient cancellation in the cyclic prefix (CP) region. When the propagation delay between two communicating radios is non-zero, the CP noise affects the data region of the desired signal. In this work, we propose CP noise reduction (CPNR) technique for OFDM based IBFD radios. We have evaluated CPNR with time and frequency domain DSIC schemes via both simulations and MATLAB and FPGA implementations on our Software Defined Radio based IBFD radio. In the laboratory tests, the total suppression of the IBFD radio is improved by 6 dB by employing CPNR in frequency-domain DSIC and EVM for bidirectional communication is improved by up to 5%, allowing realistic propagation delays. In addition to improving the total suppression and EVM performance, CPNR is also shown to enhance the multi-path resiliency of DSIC techniques.
Connectivity-Based Machine Learning Compact Models for Interconnect Parasitic Capacitances

A novel neural-networks parasitic extraction modeling methodology for interconnect parasitic capacitances is developed in rule-based extractors. The current rule-based extractors rely on thousands of parasitic capacitance formulas, each covering few or very limited set of interconnect patterns. These formulas also typically suffer from large errors in corner cases. The proposed methodology provides compact cross-section neural-network models that predict parasitic coupling capacitances for many diverse metal arrangements considering metals connectivity. These models significantly improve the accuracy of rule-based extraction methods. Also, they significantly reduce the pattern mismatches in traditional rule-based methods. The inputs to the proposed compact models are: dimensions of a layout pattern, aggressor polygons, and the required victim polygons for a certain process stack. Two different pattern representations are proposed to be used as inputs to neural-networks models: ratio-based and dimensions-based representations. The proposed methodology shows superior characteristics as compared to traditional existing models in four ways. First, it has high pattern coverage. Second, it mitigates the pattern mismatches. Third, it provides compact, descriptive, and accurate cross-section parasitic models. Fourth, it can handle the increasing accuracy requirements in advanced nodes. The proposed methodology is tested over three test chips of 28nm process node with more than 4.8M interconnect structures. The proposed methodology managed to significantly reduce the pattern mismatches and provided outstanding results as compared to field-solvers with an average error < 0.1% and a standard deviation < 3.2%.
A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop

Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous weedicide spray through aerial spraying units can help save water, weedicide chemical and effect less on human health. Such systems require multi-spectral cues to classify crop, weed, and soil surface. Our focus in this paper is on the detection of weeds in the sugar beet crop, using air-borne multispectral camera sensors, which is considered as an alternative crop to sugarcane to obtain sugar in Pakistan. We developed a new framework for weed identification; a patch-based classification approach as appose to semantic segmentation that is more realistic for real-time intelligent aerial spraying systems. Our approach converts 3-class pixel classification problem into a 2-class crop-weed patch classification problem which in turns improves crop and weed classification accuracy. For classification, we developed a new VGG-Beet convolutional neural network (CNN), which is based on generic VGG16 (visual graphics group) CNN model with 11 convolutional layers. For experiments, we captured a sugar beet dataset with 3-channel multispectral sensor with a ground sampling distance (GSD) of 0.2 cm/pixel and a height of 4 meters. For better comparison, we used two publicly available sugar beet crop aerial imagery datasets, captured using a 5-channel multispectral sensor and a 4-Channel multispectral sensor with a ground sampling distance of 1cm and a height of 10 meters. We observed that patch-based method is more robust to different lighting conditions. To produce low cost weed detection system usage of Agrocam sensor is recommended, for higher accuracy Red Edge and Sequoia multispectral sensors with more channels should be deployed. We observed higher crop-weed accuracy and lower testing time for our patch-based approach as compared to U-Net and Deeplab based semantic segmentation networks.
Enhancement of Coded Speech Using Neural Network-Based Side Information

Audio codecs generate notable artifacts when operating at low bitrates, which degrade the quality of the coded audio significantly. There have been several approaches to enhance the quality of decoded signals with and without side information. While pre- or post-processing approaches without side information can be applied directly to existing systems without modifying codecs, approaches utilizing side information can further enhance the performance while maintaining backward-compatibility with existing codecs. In this paper, we propose a method to improve decoded signals using neural network-based side information. A neural network in the transmitter side that generates the side information and another neural network in the receiver side that estimates the log power spectra (LPS) of the original signal from the decoded signal and the side information are jointly trained to accurately reconstruct the original signal. In the same line with the analysis-by-synthesis, the neural network that generates the side information in the transmitter side takes not only the LPS of the original signal but also the LPS of the decoded signal as the input by decoding the encoded bitstream at the transmitter side. Experimental results show that the proposed audio codec enhancement scheme using neural network-based side information outperformed the audio codec enhancement without side information for the same codec operating at higher bitrates.
Reemergence of yellow fever virus in southeastern Brazil, 2017-2018: what sparked the spread?

Background The 2017-2018 yellow fever virus (YFV) outbreak in southeastern Brazil marked a reemergence of YFV in urban states that had been YFV free for nearly a century. Unlike earlier urban YFV transmission, this epidemic was also driven by forest mosquitos. The objective of this study was to evaluate environmental drivers of this outbreak. Methodology/Principal Findings Using surveillance data from the Brazilian Ministry of Health of human and non-human primate (NHP) cases of yellow fever, we traced the spatiotemporal progression of the outbreak. We then assessed the epidemic timing in relation to drought using a monthly Standardized Precipitation Evapotranspiration Index (SPEI). Lastly, we evaluated demographic risk factors for rural or outdoor exposure amongst YFV cases. Both human and NHP cases were first identified in a hot, dry, rural area in northern Minas Gerais before spreading southeast into the more cool, wet urban states of Espírito Santo, São Paulo, and Rio de Janeiro. Outbreaks also coincided with drought in all four southeastern states of Brazil. Confirmed YFV cases had an increased odds of being male (OR 2.58; 95% CI 2.28-2.92), working age (OR: 2.03; 95% CI: 1.76-2.35), and reporting recent travel from an urban to a rural area (OR: 5.02; 95% CI: 3.76-6.69). Conclusions/Significance The 2017-2018 YFV epidemic in Brazil originated in hot, dry rural areas of Minas Gerais before expanding south into urban centers. An unusually severe drought in this region may have created environmental pressures that sparked the reemergence of YFV in Brazil’s southeastern cities. Author Summary In 2017-2018, cities in southeastern Brazil experienced an unusual outbreak of yellow fever virus. In the early 20th century, these cities had large outbreaks of yellow fever, spread by Aedes mosquitos. But until this recent outbreak, they had been free of yellow fever for nearly a century. While this outbreak was spread by Haemagogous forest mosquitos, the reemergence of yellow fever in densely populated urban areas raises serious concerns about it reestablishing ongoing transmission in cities, spread by urban Aedes mosquitos. Our study sought to understand how and why yellow fever virus remerged in this area. We traced the outbreak, finding that it started in hot, dry, rural areas and spread south into cool, wet urban areas. Additionally, the outbreak coincided with a severe drought; this extreme weather may have promoted the spread of yellow fever. Infection was also associated with rural and outdoor exposure, further suggesting this epidemic originated in rural areas.
Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18 F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterisation of the human brain. How these two modalities differ in their contribution to cognition and behaviour is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that while for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, while for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.
Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons

Learning entails preserving the features of the external world in the neuronal representations of the brain, and manifests itself in the form of strengthened interactions between neurons within assemblies. Hebbian synaptic plasticity is thought to be one mechanism by which correlations in spiking promote assembly formation during learning. While spike timing dependent plasticity (STDP) rules for excitatory synapses have been well characterized, inhibitory STDP rules remain incomplete, particularly with respect to sub-classes of inhibitory interneurons. Here, we report that in layer 2/3 of the orbitofrontal cortex of mice, inhibition from parvalbumin (PV) interneurons onto excitatory (E) neurons follows a symmetric STDP function and mediates homeostasis in E-neuron firing rates. However, inhibition from somatostatin (SOM) interneurons follows an asymmetric, Hebbian STDP rule. We incorporate these findings in both large scale simulations and mean-field models to investigate how these differences in plasticity impact network dynamics and assembly formation. We find that plasticity of SOM inhibition builds lateral inhibitory connections and increases competition between assemblies. This is reflected in amplified correlations between neurons within assembly and anti-correlations between assemblies. An additional finding is that the emergence of tuned PV inhibition depends on the interaction between SOM and PV STDP rules. Altogether, we show that incorporation of differential inhibitory STDP rules promotes assembly formation through competition, while enhanced inhibition both within and between assemblies protects new representations from degradation after the training input is removed.
Molecular phylogeny of fucoxanthin-chlorophyll a/c proteins from Chaetoceros gracilis and Lhcq/Lhcf diversity

Diatoms adapt to various aquatic light environments and play major roles in the global carbon cycle using their unique light-harvesting system, i.e., fucoxanthin chlorophyll a/c binding proteins (FCPs). Structural analyses of photosystem II (PSII)-FCPII and photosystem I (PSI)-FCPI complexes from the diatom Chaetoceros gracilis have revealed the localization and interactions of many FCPs; however, the entire set of FCPs has not been characterized. Here, we identified 46 FCPs in the newly assembled genome and transcriptome of C. gracilis. Phylogenetic analyses suggested that these FCPs could be classified into five subfamilies: Lhcr, Lhcf, Lhcx, Lhcz, and novel Lhcq, in addition to a distinct type of Lhcr, CgLhcr9. The FCPs in Lhcr, including CgLhcr9 and some Lhcqs, had orthologous proteins in other diatoms, particularly those found in the PSI-FCPI structure. By contrast, the Lhcf subfamily, some of which were found in the PSII-FCPII complex, seemed to be diversified in each diatom species, and the number of Lhcqs differed among species, indicating that their diversification may contribute to species-specific adaptations to light. Further phylogenetic analyses of FCPs/light-harvesting complex (LHC) proteins using genome data and assembled transcriptomes of other diatoms and microalgae in public databases suggest that our proposed classification of FCPs was common among various red-lineage algae derived from secondary endosymbiosis of red algae, including Haptophyta. These results provided insights into the loss and gain of FCP/LHC subfamilies during the evolutionary history of the red algal lineage. One sentence summary Phylogenetic analysis of fucoxanthin-chlorophyll a/c proteins in C. gracilis revealed five major subfamilies and one minor subfamily, providing insights into the diversification of light-harvesting systems in red algae.
Differentially optimized cell-free buffer enables robust expression from unprotected linear DNA in exonuclease-deficient extracts

The use of linear DNA templates in cell-free systems promises to accelerate the prototyping and engineering of synthetic gene circuits. A key challenge is that linear templates are rapidly degraded by exonucleases present in cell extracts. Current approaches tackle the problem by adding exonuclease inhibitors and DNA-binding proteins to protect the linear DNA, requiring additional time- and resource-intensive steps. Here, we delete the recBCD exonuclease gene cluster from the Escherichia coli BL21 genome. We show that the resulting cell-free systems, with buffers optimized specifically for linear DNA, enable near-plasmid levels of expression from σ70 promoters in linear DNA templates without employing additional protection strategies. When using linear or plasmid DNA templates at the buffer calibration step, we found that the optimal potassium glutamate concentrations obtained when using linear DNA were consistently lower than those obtained when using plasmid DNA for the same extract. We demonstrate the robustness of the exonuclease deficient extracts across seven different batches and a wide range of experimental conditions. Finally, we illustrate the use of the ΔrecBCD extracts for two applications: toehold switch characterization and enzyme screening. Our work provides a simple, efficient, and costeffective solution for using linear DNA templates in cell-free systems and highlights the importance of specifically tailoring buffer composition for the final experimental setup. Our data also suggest that similar exonuclease deletion strategies can be applied to other species suitable for cell-free synthetic biology.
Greater Neural Pattern Dissimilarity in the Retrieval Stage of Spaced Learning is Associated with Higher Meaningfulness and Better Memory

Spaced learning research aims to find the best learning interval to improve people's learning efficiency. Although it has been studied for many years, people do not know the physiological mechanism behind it clearly. Some neural representation studies have found that under the condition of spaced learning, the representation similarity between two encoding stages increases, and believe that this conflicts with the classical encoding variability theory (Xue et al., 2010) In this experiment, we used a new experimental paradigm and a longer lag between two studies to let the Chinese university students learn English-Chinese word pairs, and we introduced the idea of meaningful learning into the learning stage to improve the traditional keyword method elaboration task. Finally, by comparing the difference between (spaced learning one-time learning) and (massed learning one-time learning) in the final test (retrieval) stage, we get results more fitting to the classical theories: there is no significant difference in event-related potential (ERP). At the same time, the spatiotemporal pattern dissimilarity (STPDS) had significant results in the parietal lobe of 400ms and the right frontal lobe of 600ms. However, we believe that there is no direct contradiction between our experimental evidence and the experiment mentioned before. On the contrary, they reflect different aspects of the process of spaced learning. Based on the different neural representation evidence of encoding and retrieval, this paper presents an idea of reintegrating three classical theories, eliminating the opposition between encoding variability theory and deficient processing theory. We also summarized and classified the experimental paradigm of spaced learning systematically.
Hi-C Contacts Encode Heterogeneity in Sub-diffusive Motion of E. coli Chromosomal Loci

Underneath its apparently simple architecture, the circular chromosome of E. coli is known for displaying complex dynamics in its cytoplasm. Recent experiments have hinted at an inherently heterogeneous dynamics of chromosomal loci, the origin of which has largely been elusive. In this regard, here we investigate the loci dynamics of E. coli chromosome in a minimally growing condition at 30°C by integrating the experimentally derived Hi-C interaction matrix within a computer model. Our quantitative analysis demonstrates that, while the dynamics of the chromosome is sub-diffusive in a viscoelastic media in general, the diffusion constants and the diffusive exponents are strongly dependent on the spatial coordinates of chromosomal loci. In particular, the loci in Ter Macro-domain display slower mobility compared to the others. The result is found to be robust even in the presence of active noise. Interestingly, a series of control investigations reveal that the absence of Hi-C interactions in the model would have abolished the heterogeneity in loci diffusion, indicating that the observed coordinate-dependent chromosome dynamics is heavily dictated via Hi-C-guided longrange inter-loci communications. Overall, the study underscores the key role of Hi-C interactions in guiding the inter-loci encounter and in modulating the underlying heterogeneity of the loci diffusion.