Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of E. coli RNA polymerase transcription elongation complexes for systematic RNA assays

RNA folds into secondary and tertiary structures that can mediate diverse cellular functions. Understanding how RNA sequence directs the formation of biologically active structures requires approaches that can comprehensively assess how changes in an RNA sequence affect its structure and function. Towards this goal, I have developed a general method for purifying E. coli RNA polymerase (RNAP) transcription elongation complexes (TECs) for use in systematic RNA assays. My approach depends on two constituent technologies: First, I have designed an E. coli σ 70 promoter that can be efficiently barcoded using a one-pot series of enzymatic reactions. Second, I have developed a strategy for purifying promoter-initiated E. coli RNAP TECs by selective photo-elution from streptavidin-coated magnetic beads. Together, these methods establish a platform for the development of TEC Display assays in which the functional properties of RNA sequence variants can be recorded by fractionating and quantitatively barcoding a TEC library.
A detailed discussion of the N2(C 3Πu) and N2(X 1Σg+) vibrational temperatures in N2 glow discharges

A N 2 direct current discharge was studied by optical emission spectroscopy. Experimental parameters as gas flow rate (Q) , pressure (p) and current (I) were varied independently, covering the following range: 10 I Q p 9 −10 12 cm −3 and reduced electric field between 3×10 −16 and 10 −15 V cm −2 . The N 2 (C ), N 2 (X ) vibrational temperatures and the gas temperature were obtained from the first and second positive systems emissions. The N 2 (C ) vibrational distributions did not exhibit a Boltzmann character. A linearization process was employed to such distributions allowing us to calculate the N 2 (C ) temperature. The N 2 (X ) temperature was calculated from the N 2 (C ) one on the basis of the Franck-Condon factors. Results and the method of vibrational temperature calculus are presented.
Updating the steady-state model of C4 photosynthesis

C4 plants play a key role in world agriculture. For example, C4 crops such as maize and sorghum are major contributors to both first and third world food production and the C4 grasses sugarcane; miscanthus and switchgrass are major plant sources of bioenergy. In the challenge to manipulate and enhance C4 photosynthesis, steady state models of leaf photosynthesis provide and important tool for gas exchange analysis and thought experiments that can explore photosynthetic pathway changes. Here the C4 photosynthetic model by von Caemmerer and Furbank (1999) has been updated with new kinetic parameterisation and temperature dependencies added. The parameterisation was derived from experiments on the C4 monocot, Setaria viridis, which for the first time provides a cohesive parametrisation. Mesophyll conductance and its temperature dependence have also been included, as this is an important step in the quantitative correlation between the initial slope of the CO2 response curve of CO2 assimilation and in vitro PEP carboxylase activity. Furthermore, the equations for chloroplast electron transport have been updated to include cyclic electron transport flow and equations have been added to calculate electron transport rate from measured CO2 assimilation rates. Highlight The C4 photosynthesis model by von Caemmerer and Furbank (1999) has been updated. It now includes temperature dependencies and equations to calculate electron transport rate from measured CO2 assimilation rates.
Furfural and ageing: how are they related

Cellulose degradation is a complex process which is accelerated by the combined effect of temperature, water and oxygen, all of them available to the cellulose in the environment of the transformer. Several mechanisms for the thermal degradation of cellulose have been proposed, depending on whether the predominant step is pyrolysis or hydrolysis of the glucose units. In the first case, pyrolysis of glucose, the reaction proceeds via levoglucosan (1,6-anhydro-P-D-glucopyranose) a dehydrated sugar which, after a series of bond rearrangements and further losses of water and formaldehyde (or hydrogen plus carbon monoxide) yield furfuraldehyde among other products. The hydrolysis of glucose units proceeds through the formation of an epoxide or an enol followed by internal rearrangements and further dehydration and elimination of formaldehyde to yield furfuraldehyde and other products. The connection between the deterioration in the material properties and the formation of ageing products is the degree of polymerisation, the number of anhydroglucose units in the cellulose chain. The degree of polymerisation proves to be the most informative parameter for assessing the ageing or the progress of ageing of cellulose.
Synthesis of Monodisperse Silica Nanoparticles Dispersable in Non‐Polar Solvents

Three synthetic routes to hydrophobic silica nanoparticles are compared in this paper. First, the established synthetic method based on the Stöber process was examined. Monodisperse colloidal silica particles with diameters of 15–25 nm were prepared via the hydrolysis of tetraethyl orthosilicate (TEOS) by aqueous ammonia in ethanol. The surfaces of these particles were rendered hydrophobic with octadecyltrimethoxysilane (ODTMS) after the reaction or, more conveniently, during the growth phase. Secondly, silica particles with diameters of 15–50 nm were prepared using a one‐pot synthesis in which TEOS was hydrolyzed by an amino acid and the resulting particles were coated with ODTMS. Lastly a novel, direct approach to the synthesis of hydrophobic organosilica nanoparticles was developed using ODTMS as the single silica source. Hydrolysis of the ODTMS by aqueous ammonia in ethanol yielded monodisperse colloidal organosilica particles with diameters of 15–30 nm.
On ISRC Rumor Spreading Model for Scale-Free Networks with Self-Purification Mechanism

At present, the feasibility of using self-purification mechanism to inhibit rumor spreading has been confirmed by studies from different perspectives. ,is paper improves the classical rumor spreading models with self-purification mechanism, analyzes the correlation between spreading threshold in the model and its self-purification level theoretically, and conducts numerical simulations to study the impact of the changes of model parameters on key indicators in the process of rumor spreading. ,e simulation results show that changes of model parameters, including self-purification level and forgetting rate, exert significant influences on rumor spreading exactly.
Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information.We have conducted a lot of experiments on six commonly used benchmark datasets (AWA1, AWA2, APY, FLO, SUN, and CUB). -e experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zeroshot learning.
Fixed-point algorithms for inverse of residual rectifier neural networks

A deep neural network with invertible hidden layers has a nice property of preserving all the information in the feature learning stage. In this paper, we analyse the hidden layers of residual rectifier neural networks, and investigate conditions for invertibility under which the hidden layers are invertible. A new fixed-point algorithm is developed to invert the hidden layers of residual networks. The proposed inverse algorithms are capable of inverting some residual networks which cannot be inverted by existing inverting algorithms. Furthermore, a special residual rectifier network is designed and trained on MNIST so that it can achieve comparable performance with the state-of-art performance while its hidden layers are invertible.
AdCom: Adaptive Combiner for Streaming Aggregations

Continuous applications such as device monitoring and anomaly detection often require real-time aggregated statistics over un-bounded data streams. While existing stream processing systems such as Flink, Spark, and Storm support processing of streaming aggregations, their optimizations are limited with respect to the dynamic nature of the data, and therefore are suboptimal when the workload changes and/or when there is data skew. In this paper we present AdCom , which is an adaptive combiner for stream processing engines. The use of AdCom in aggregation queries enables pre-aggregating tuples upstream (i.e., before data shuffling) followed by global aggregation downstream. In contrast to existing approaches, AdCom can automatically adjust the number of tuples to pre-aggregate depending on the data rate and available network. Our experimental study using real-world streaming workloads shows that using AdCom leads to 2 . 5–9 × higher sustainable throughput without compromising latency.
Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.
An Efficient and Secure Location-based Alert Protocol using Searchable Encryption and Huffman Codes

Location data are widely used in mobile apps, ranging from location-based recommendations, to social media and navigation. A specific type of interaction is that of location-based alerts, where mobile users subscribe to a service provider (SP) in order to be notified when a certain event occurs nearby. Consider, for instance, the ongoing COVID-19 pandemic, where contact tracing has been singled out as an effective means to control the virus spread. Users wish to be notified if they came in proximity to an infected individual. However, serious privacy concerns arise if the users share their location history with the SP in plaintext. To address privacy, recent work proposed several protocols that can securely implement location-based alerts. The users upload their encrypted locations to the SP, and the evaluation of location predicates is done directly on ciphertexts. When a certain individual is reported as infected, all matching ciphertexts are found (e.g., according to a predicate such as “10 feet proximity to any of the locations visited by the infected patient in the last week”), and the corresponding users notified. However, there are significant performance issues associated with existing protocols. The underlying searchable encryption primitives required to perform the matching on ciphertexts are expensive, and without a proper encoding of locations and search predicates, the performance can degrade a lot. In this paper, we propose a novel method for variable-length location encoding based on Huffman codes. By controlling the length required to represent encrypted locations and the corresponding matching predicates, we are able to significantly speed up performance. We provide a theoretical analysis of the gain achieved by using Huffman codes, and we show through extensive experiments that the improvement compared with fixed-length encoding methods is substantial. © 2021 Copyright held by the owner/author(s).
Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia

The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions The response to prevent and control the new coronavirus pneumonia has reached a crucial point Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality Recently, deep learning models are playing essential roles in handling time-series data in different applications This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19 Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020 The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait
Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity.
Metal Oxide Nanoparticles as Bactericidal Agents

Reactive magnesium oxide nanoparticles and halogen (Cl2, Br2) adducts of these MgO particles were allowed to contact certain bacteria and spore cells. Bacteriological test data, atomic force microscopy (AFM) images, and electron microscopy (TEM) images are provided, which yield insight into the biocidal action of these nanoscale materials. The tests show that these materials are very effective against Gram-positive and Gram-negative bacteria as well as spores. ζ-Potential measurements show an attractive interaction between the MgO nanoparticles and bacteria and spore cells, which is confirmed by confocal microscopy images. The AFM studies illustrate considerable changes in the cell membranes upon treatment, resulting in the death of the cells. TEM micrographs confirm these results and supply additional information about the processes inside the cells. Overall, the results presented illustrate that dry powder nanoparticulate formulations as well as water slurries are effective. It is proposed that abrasive.
Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.
Adaptive Radar Waveform Design Based on Weighted MI and the Difference of Two Mutual Information Metrics

*is study deals with the problem of radar waveform design based on the weighted mutual information (MI) and the difference of two mutual information metrics (DMI) in signal-dependent interference. Since the target and clutter information are included in the received signal at the beginning of the design, DMI-based waveform is designed according to the following criterion: maximizing the MI between the received signal and target impulse response while minimizing the MI between the received signal and the clutter impulse response. *is criterion is equivalent to maximizing the difference between the first MI and the secondMI. *en maximizing the difference of two types of MI is used as the objective function, and the optimization model with the transmitted waveform energy constraint is established. In order to solve it, we resort to maximum marginal allocation (MMA) method to find the DMI-based waveform. Since DMI-based waveform does not allocate energy to the frequency band where the clutter power spectral density (PSD) is greater than the target PSD, we propose to weight the MI-based waveform and DMI-based waveform to synthesize the final optimal waveform. It could provide different trade-offs between two types of MI. Simulation results show the proposed algorithm is valid.
JENGA - A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models

Machine learning (ML) is increasingly used to automate decision making in various domains. Almost all common ML models are susceptible to data errors in the serving data (for which the model makes predictions). Such errors frequently occur in practice, caused for example by program bugs in data preprocessing code or non-anticipated schema changes in external data sources. These errors can have devastating effects on the prediction quality of ML models, and are, at the same time, hard to anticipate and capture. In order to empower data scientists to study the impact as well as mitigation techniques for data errors in ML models, we pro-pose Jenga, a light-weight, open source, experimentation library. Jenga allows its users to easily test their models for robustness against common data errors. Jenga contains an abstraction for prediction tasks based on a dataset and a model, an easily extend-able set of synthethic data corruptions (e.g., for missing values, outliers, typos and noisy measurements) as well as evaluation functionality to experiment with different data corruptions. Jenga supports researchers and practitioners in the difficult task of data validation for ML applications. As a showcase for this, we discuss two use cases of Jenga: studying the robustness of a model against incomplete data, as well as automatically stress testing integrity constraints for ML data expressed with tensorflow data validation.
Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Communication and Computers Engineering Department Alshrouck Academy, Cairo, Egypt Computer Engineering Department, Collage of Comp. Science and Engineering, Taibah University, Medina, Saudi Arabia College of Computer Science and Engineering, University of Hai’l, Hai’l, Saudi Arabia Arab Academy for Science Technology and Maritime Transport, Cairo, Egypt Computer and System Engineering Department, Al-Azhar University, Cairo, Egypt Computer Engineering Department, Cairo Universality, Cairo, Egypt
Virus-Information Coevolution Spreading Dynamics on Multiplex Networks

Virus and information spreading dynamics widely exist in complex systems. However, systematic study still lacks for the interacting spreading dynamics between the two types of dynamics. This paper proposes a mathematical model on multiplex networks, which considers the heterogeneous susceptibility and infectivity in two subnetworks. By using a heterogeneous mean-field theory, we studied the dynamic process and outbreak threshold of the system. Through extensive numerical simulations on artificial networks, we find that the virus’s spreading dynamics can be suppressed by increasing the information spreading probability, decreasing the protection power, or decreasing the susceptibility and infectivity.