Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterologous expression of the immune receptor EFR in Medicago truncatula reduces pathogenic infection, but not rhizobial symbiosis

Interfamily transfer of plant pattern recognition receptors (PRRs) represents a promising biotechnological approach to engineer broad-spectrum, and potentially durable, disease resistance in crops. It is however unclear whether new recognition specificities to given pathogen-associated molecular patterns (PAMPs) affect the interaction of the recipient plant with beneficial microbes. To test this in a direct reductionist approach, we transferred the Brassicaceae-specific PRR ELONGATION FACTOR-THERMO UNSTABLE RECEPTOR (EFR) from Arabidopsis thaliana to the legume Medicago truncatula, conferring recognition of the bacterial EF-Tu protein. Constitutive EFR expression led to EFR accumulation and activation of immune responses upon treatment with the EF-Tu-derived elf18 peptide in leaves and roots. The interaction of M. truncatula with the bacterial symbiont Sinorhizobium meliloti is characterized by the formation of root nodules that fix atmospheric nitrogen. Although nodule numbers were slightly reduced at an early stage of the infection in EFR-Medicago when compared to control lines, nodulation was similar in all lines at later stages. Furthermore, nodule colonization by rhizobia, and nitrogen fixation were not compromised by EFR expression. Importantly, the M. truncatula lines expressing EFR were substantially more resistant to the root bacterial pathogen Ralstonia solanacearum. Our data suggest that the transfer of EFR to M. truncatula does not impede root nodule symbiosis, but has a positive impact on disease resistance against a bacterial pathogen. In addition, our results indicate that Rhizobium can either avoid PAMP recognition during the infection process, or is able to actively suppress immune signaling. Significance Statement Crop engineering helps reducing the economic and environmental costs of plant disease. The genetic transfer of immune receptors across plant species is a promising biotechnological approach to increase disease resistance. Surface-localized pattern-recognition receptors (PRRs), which detect conserved characteristic microbial features, are functional in heterologous taxonomically-diverse plant species, and confer broad-spectrum disease resistance. It was unclear whether PRR transfer negatively impacts the association of the recipient plants with symbiotic microbes. Here, we show that a legume engineered with a novel PRR recognizing a conserved bacterial protein becomes more resistant to an important bacterial pathogen without significant impact on nitrogen-fixing symbiosis with rhizobia. This finding is of particular relevance as attempts to transfer this important symbiosis into non-legume plants are ongoing.
Analysis of Adversarial based Augmentation for Diabetic Retinopathy Disease Grading

Diabetic Retinopathy(DR) is one of the diseases, which is caused by damage to blood vessels of the light sensitive tissue of the eyes. DR is graded into five different levels; normal, mild, moderate, severe and proliferative. DR diagnosis demands more time on detection from fundus images. An accurate automatic model requires sufficient data for training which is unavailable. The open-source DR datasets are highly imbalanced between the levels of DR and it is also not easy to collect more data for proliferative cases. The synthetic generation of data for such highly imbalanced classes in a dataset provides better results on classification. In this paper, an analysis of classification results for the same is carried out with augmented proliferative class (highly imbalanced class) in the EYEPACS dataset using a generative adversarial network (GAN). We generated highly diverse images for proliferative cases without any constraints. The generated proliferative images do not influence other class images and have also improved the classification results obtained by the model, over that which was trained without synthetic generation. The results obtained before and after augmentation by the proposed generative based model is compared over various model attributes.
The Phylogenetic Reconstruction of the Grass Family (Poaceae)Using matK Gene Sequences

Comparative DNA sequencing of matK, a maturase-encoding gene located within the intron of the chloroplast trnK gene, was evaluated for phylogenetic utility above the family level and within the grass family (Poaceae). There are three major objectives in the research. The first one is to study the utility of the matK gene in plant evolution. The second objective is to characterize the matK gene in the grass family. The last major goal is to address the phylogenetic questions in the Poaceae using the matK sequences from representatives of different grass groups. In order to study the potential application of matK to plant systematics above the family level, eleven complete sequences from GenBank representing seed plants and liverworts and nine partial sequences generated for genera representing the monocot families Poaceae, Joinvilleaceae, Cyperaceae, and Smilacaceae were analyzed. The study underscored the following useful properties of the matK gene for phylogenetic reconstruction: reasonable size (1500 bp), high rate of substitution, large proportion of variation at the first and the second codon positions, low transition-transversion ratio, and the presence of mutationally-conserved sectors. The use of different sectors of the gene and the cumulative inclusion of informative sites showed that the 3’ region was the most useful in resolving phylogeny, and that the topology and robustness of the tree reached a plateau after the inclusion of 100 informative sites. The presence of a relatively conserved 3’ region and the less conserved 5’ region provides two sets of characters that can be used at different taxonomic levels from the tribal to the division levels. It also has demonstrated the potential of partial sequencing in resolving systematic relationships from the tribe to the division level. The matK gene in the Poaceae was characterized with complete sequences from 11 grass genera, representing 7 subfamilies and 11 tribes, and one outgroup (Joinvillea plicata, Joinvillaceae). The alignment of 1632 base pairs from 14 species yielded a data set of 601 (36.8)% variable sites and 246 (15.1%) informative sites. The variations at nucleic and amino acid levels evenly distributed throughout the entire gene, and the 5’ region appears to have more variation than the 3’ region. The changes at the third codon position are very low as compared to the total of the first and second positions. This has led to a similar variation pattern at nucleic and at amino acid levels. The average tr/tv ratio
Utilizing Exploratory Data Analysis for the Prediction of Campus Placement for Educational Institutions

In Exploratory Data Analysis (EDA) the given large data is visually analyzed to extract the embedded deep. Application of the technique has a wide range and aids in the informed decision making abilities of the managers. In an educational institution, the success of its imbibing model is usually measured using the career opportunities of the graduates. Hence, the placement data has an important relevance for the future plan and growth. Quite a good amount of information can be gained by all the stakeholder by carefully looking at this information. In this context, the technique of EDA can be used to visually analyze the placement of students in a higher educational institution. In this paper the data about the placement of student is visually analyzed to generate inferences using mathematical models. Based on the study it was found that student with MBA specialization in Mkt&Fin are highly placed, a vast majority of the students have Commerce and Management degrees. The score on the employability test don't seem to have a major impact on the placement of students.
Hyper Neural Network as the Diffeomorphic Domain for Short Code Soft Decision Beyound Belief Propagation Decoding Problem

We proposed topological interpretation of the Tanner–Forney–Gross–Nachmani’s Hyper Graph soft decoders based on Sourlas’s Spin Glass reduction and Mezard’s Replica Symmetry Breaking. Using it, we demonstrated reasons for uncertainty of the Neural Network loss function landscape and efficiency of replacing the arctanh neural network activation function with the Nishimori Temperature arctanh Taylor approximation. We compare the performance of short-length best known linear binary codes from Brouwer–Grassl codetable, Polar codes with sequence of frozen bits designed by Gaussian approximation and 5G eMBB Multi-Edge Type LDPC code with Base Graph 2 protograph on the AWGN-channel. The Sum-Product Flooding Scheduler decoder 50 iteration, Afterburn Saturated Min-Sum decoder, Ordered Statistics Decoder, Successive Cancellation Decoder with List size 32, Hyper Graph Neural Network under unfolding Belief Propagation decoder with Activation function Continues Metric Space relaxation according Nishimura temperature are used. The obtained simulation results are compared with the Finite-Length Polyanskiy theoretical boundary.
Nutritional Aspects of Food Toxicology: Mercury Toxicity and ProtectiveEffects of Olive Oil Hydroxytyrosol

Mercury represents one of the main environmental pollutants and human exposure to this heavy metal occurs primarily through nutritional sources, including contaminated fish. This highly toxic compound is known to pose serious threats to human health, including neurological alterations. Moreover, based on its effects on cardiovascular health, mercury exposure is now considered an independent risk factor for cardiovascular diseases. The possibility of reducing heavy metal toxicity through diet has attracted the interest of those responsible for the public health service. In this respect, the use of phytochemicals able to significantly counteract oxidative alterations as an attractive tool for the reduction of mercury toxicity has been proposed. Here we review recent evidence supporting the beneficial role of olive oil hydroxytyrosol in preventing mercury-induced alterations in both human erythrocytes and neuroblastoma cells. This novel biological effect exerted by hydroxytyrosol represents an additional mechanism responsible for the much-claimed health benefits of this dietary phenol. Taken together the reported findings encourage the use of virgin olive oil, characterized by a high hydroxytyrosol content, as an innovative approach in designing combined dietary and/or nutraceutical strategies to contrast mercury toxicity in humans.
Model Predictive Control of Grid-connected Converter Based on Virtual Synchronous Machine

With the increase of distributed power generation integrated into the power grid through power electronic converters, the problem of inertia reduction in power system has been caused, a new control technology is needed to enable distributed power sources to actively participate in the voltage and frequency regulation of the grid. In this paper, a model predictive control (MPC) strategy based on virtual synchronous machine (VSM) for the grid-connected converter is proposed. The method does not require traditional pulse width modulation and current inner loop control. When the voltage and frequency of power grid change, the active power and reactive power can respond in time and simulate characteristics of traditional synchronous machine. The simulation results are given to verify the effectiveness of the control strategy presented above.
Personal safety analysis of live working in 10kV distribution network based on finite element method

In order to reduce the power outage time caused by the maintenance and replacement of equipment on the transmission line, live working is widely used in the operation and maintenance of distribution network in China. Because the line is in a live operation state in this mode of operation, the operators are under potential safety risks on the line. In the event of an accident, their personal safety is threatened. In this paper, the electric shock mechanism of 10kV distribution network under live operation is studied. The simulation software Ansys/Maxwell is used to build the distribution network line and tower model. The electric field strength and potential distribution of the charged working space and the operator's body surface are analyzed based on finite element method.
User Experience and HMI Technologies for the Future

The nature of the automotive industry is changing as Connectivity, Autonomous Drive, Shared Mobility concepts and Electrification take center stage in this new decade. This transformation will drive some unique needs and present some interesting challenges in the years ahead. In this paper we will explore how the User Experience, and the HMI technologies that enable it, will define how we perceive the future of mobility.
The hypoxic metabolic response : how time and PO₂ shape the way fishes combine aerobic, anaerobic and depressed metabolism in hypoxic environments

ii LAY SUMMARY . iv PREFACE . v TABLE OF CONTENTS . vi LIST OF TABLES . x LIST OF FIGURES . xi ACKNOWLEDGEMENTS . xiii DEDICATION . xv CHAPTER 1: General introduction . 1 1.1 Environmental hypoxia . 2 1.2 The metabolic responses of fishes to hypoxia . 3 1.2.1 Aerobic metabolism . 3 1.2.2 Anaerobic metabolism . 5 1.2.3 Metabolic rate depression . 6 1.3 The hypoxic metabolic response: the concurrent use of aerobic metabolism, anaerobic metabolism and metabolic rate depression in hypoxic environments . 8 1.4 Thesis objectives and hypotheses . 9 CHAPTER 2: A simple and affordable calorespirometer for measuring the metabolic rates of fishes . 13 2.1 Summary . 13 2.2 Introduction . 14 2.3 Materials and methods . 15 2.3. 1 Theory and overview . 15 2.3. 2 Calorimeter . 16
Intrusion classification using ECLAT and Fuzzy Logic

In today's world with the increase in internet usage, every digital gadget is getting connected to the internet. Due to the open connectivity of the internet, devices connected to the internet are exposed to the disturbances caused by masqueraders, misfeasors, malware writers, and intruders. Researchers are in the continuous search of methods to detect the attacks in the network and to introduce these methods to the low computation capability devices. In this work, it aimed at creating a disruption sensing system for identifying the disturbances caused by intruders. It will be achieved by choosing the best traffic capturing component. Then, by introducing/improving the association rule mining algorithm to identify the patterns in the data and to generate better rules. Then, by using the proposed method based on fuzzy logic and inference system to help in identifying the attack.
Fault Identification of Hydroelectric Sets Based on Time-frequency Diagram and Convolutional Neural Network

Aiming at the poor generalization ability of traditional hydroelectric sets fault diagnosis methods, a fault diagnosis method for hydroelectric sets based on time-frequency diagram and convolutional neural network (CNN) is proposed. First, a continuous wavelet transform (CWT) is performed on the vibration signal of the hydroelectric sets to construct a time-frequency diagram. Then, combined with the CNN, the fault state identification of the hydroelectric sets is realized. The method realizes the automatic extraction of the texture features of the time-frequency diagram, avoids manual identification, and can quickly and accurately identify the state of the hydroelectric sets. The results show that the method can effectively identify the type of fault.
A Survey on Decision Support Systems in Water Management

Water is one of the most important resources for survival and it is extremely necessary to make good decisions regarding its management. The technology has advanced and offers us Decision Support Systems (DSS), using complex solutions. In recent decades, the number of DSSs developed for water resource management has grown significantly, and computer systems have become more complex by integrating water management processes, Internet of things (IoT) sensors, multi-criteria decisions and why not even serious games. The involvement of several stakeholders in the decision-making process has high importance for a successful DSS, facilitating the analysis of approaches from different fields. In order to find a cost-efficient balance in managing water resources, it is necessary to reach several objectives in different fields. The current paper aims to make a brief review of technological development and integration to highlight the socio-economic benefits.
Reinforcement Learning for Anti-Ransomware Testing

In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens.
Customer Analytics using Surveillance Video

The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping $k$-Means Extension (MCOKE) algorithm with weighted $k$-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer; the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits.
Applying AI Analysis-based IoT System Control to the Individualized Learning Field

This study proposes the incorporation of AI analysis and Internet of Things (IoT) control into the individualized learning field. The data of facial image recognition, limb recognition, and brainwave concentration are integrated and used for learning behavior analysis, after which changes in the learners’ concentration index are used to adjust indoor IoT devices. In the individualized learning field, the users’ real-time face detection and brainwave status were subjected to an artificial neural network to analyze their facial expressions and brainwaves during learning to determine their level of concentration and relaxation. Additionally, environmental information acquired from air quality monitoring in the field was also analyzed. During the experiment, the learners’ level of concentration and relaxation was identified through the $\alpha$ and $\beta$ waves output by the electroencephalograph, after which facial expression recognition was performed. Following analysis, the IoT devices were adjusted to determine whether the proposed AI analysis model can effectively enhance learners’ concentration levels.
Using Dtrace for Machine Learning Solutions in Malware Detection

Malware cannot be ignored today as evidenced by the havoc created by Wannacry[29], [12], [23]. Every day malicious actors are writing more intelligent malware. The attacks evolve as actors learn to evade detection techniques, which makes malware detection a pressing need. Research is being done on the Windows platform to detect malware in many forms[55], [54], [31]. In our work, we focus on detecting Windows malware. We use Dtrace[18], a dynamic tracing framework recently introduced in Windows, to collect system call information from an affected system. We are trying to expand the utility of Dtrace to detect malware. We process the collected system-call data to extract features and to create a dataset suitable for machine learning. Using machine learning, we build a decision tree classifier and show that the model can detect malware using the sequences of system-calls made by malicious processes. We obtained an f1 score of 91 with maintaining an even class distribution between the benign and malicious labels.
Evaluation of the Effect of High-Dorsiflexion Assistive Robotic Technology on Voluntary Ankle Movement

The proposed high-dorsiflexion assistive robotic technology (RT) is aimed to assist ankle movement during gait rehabilitation. A McKibben-type artificial muscle facilitates dorsiflexion in the swing phase by lifting a forefoot of a stroke patient’s paralyzed side. In spite of the great potential of improving dorsiflexion owing to the proposed RT observed in the previous clinical results, the current approach is applicable only to the passive ankle gait rehabilitation with full assistance. It is believed that ensuring patients’ voluntary effort in rehabilitation is the key for long-term recovery. In the present research, we conducted an experiment to evaluate our RT’s effects on six healthy participants, and analyzed whether their voluntary ankle movements were affected due to the current assistance strategy. Compared with normal walk, significant decrease in processed surface electromyography signal (p = 8.62×10-3), significant early timing of a push-off motion (p =2.60×10-4), and significant smaller push-off angle (p = 1.50×10-4) were observed compared with intervention of the proposed RT. On the other hand, a significant trend of decrease in the ankle dorsiflexion rate in the swing phase is observed during intervention (p = 0.06949). Based on the obtained results, we concluded that unlike the contraction speed of an artificial muscle that did not show any considerable impact, the excessively early activation of the RT assistance affected voluntary ankle movement.
A Risk-Constrained Model for Virtual Power Plants Including DGs and Coupled Electricity-Heat Supply

To exploit the potential of flexibility from demands and distributed energy resources, this paper proposes an optimal bidding and operation strategy of virtual power plant (VPP) including a coupled electricity-heat supply system by introducing thermoelectric coupling equipment. The objective is to minimize VPP’s operation cost and risk while balancing the heat and power demand. Based on renewable power forecast scenarios, a day-ahead stochastic optimization model is proposed. The risk of operation cost is also considered by employing conditional value-at-risk (CVaR) index. Effectiveness of the proposed model is demonstrated by simulation results.
Large Intelligent Surface Assisted Wireless Communications With Spatial Modulation and Antenna Selection

Novel communication technology based on large intelligent surface (LIS) [1] has arisen recently, with the aim to enhance the signal quality at the receiver. In this paper, a practical structure of LIS-based spatial modulation (LIS-SM) is proposed, in order to utilize both transmit and receive antenna indices. Meanwhile, the theoretical average bit error rate (ABER) performance bound of the developed LIS-SM scheme is investigated. For the sake of achieving further spatial diversity gain, we extend its employment to the antenna selection (AS) scenario, and a low-complexity selection algorithm is designed on the basis of minimum squared Euclidian distance and signal-to-leakage-and-noise ratio as well as the idea of greedy elimination algorithm. Performance analysis shows that AS-aided LIS-SM is more robust in terms of ABER compared with conventional LIS-SM. Moreover, complexity analysis also depicts that the proposed fast selection algorithm achieves much lower complexity yet a comparable ABER performance, compared to the traditional exhaustive search.