Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergent Loss of an EDS1/PAD4 Signaling Pathway in Several Plant Lineages Reveals Coevolved Components of Plant Immunity and Drought Response[OPEN]

Genome analyses reveal that several plants have lost a major part of the immune system. Plant innate immunity relies on nucleotide binding leucine-rich repeat receptors (NLRs) that recognize pathogen-derived molecules and activate downstream signaling pathways. We analyzed the variation in NLR gene copy number and identified plants with a low number of NLR genes relative to sister species. We specifically focused on four plants from two distinct lineages, one monocot lineage (Alismatales) and one eudicot lineage (Lentibulariaceae). In these lineages, the loss of NLR genes coincides with loss of the well-known downstream immune signaling complex ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1)/PHYTOALEXIN DEFICIENT 4 (PAD4). We expanded our analysis across whole proteomes and found that other characterized immune genes were absent only in Lentibulariaceae and Alismatales. Additionally, we identified genes of unknown function that were convergently lost together with EDS1/PAD4 in five plant species. Gene expression analyses in Arabidopsis (Arabidopsis thaliana) and Oryza sativa revealed that several homologs of the candidates are differentially expressed during pathogen infection, drought, and abscisic acid treatment. Our analysis provides evolutionary evidence for the rewiring of plant immunity in some plant lineages, as well as the coevolution of the EDS1/PAD4 pathway and drought responses.
Deep Learning Based Multi-Label Chest X-Ray Classification with Entropy Weighting Loss

With large chest X-ray image datasets open-accessed, it is feasible to build diagnosis system based on deep learning methods. In this work, we formulated chest X-ray diagnosis as multi-label classification problem. However, multi-labels are treated independently in traditional methods and existing loss can easily lose important information for class with fewer cases. We proposed an entropy weighting loss to observe inter-label dependencies and make full use of class whose cases are fewer than others. From the global perspective of all pathological classes, digging relations between all classes, the proposed loss could improve model performance. We experimented with three basic deep learning models (VGG16, ResNet50, DenseNet121) at first, and decided to use DenseNet121 as a baseline model. We evaluated our approach and achieved better results, whose AUC score is 0.8430 on average, under the Chest X-ray14 and its patient-wise split. We demonstrate that our proposed loss can dig relations between different disease labels just as illness complications in reality, and we do not consider every label is independent.
Outage Probability of Device-to-Device Communication Underlaying Cellular Network over Nakagami/Rayleigh Fading Channels

Device-to-Device (D2D) communication allows two mobile users to communicate straight way with each other. D2D communication that underlay conventional cellular networks has been recognized as a novel approach to alleviate the outage performance and ameliorate the throughput of the traditional cellular system. The co-channel interference is the major weakness of D2D communication in D2D based cellular system. The purpose of this paper is to derive the expression of D2D link outage probability (OP) for D2D based cellular system by utilizing stochastic geometry and to evaluate the achievement of existing cellular system. The Rayleigh fading, and Nakagami-m fading have been considered for the co-channel interference signals and desired signal, respectively. Expression of OP is derived and presented in terms of the Gauss hypergeometric function. This analysis acknowledge the influence of main system parameters on outage performance of the network. Results show the D2D outage probability is depends on SIR threshold, D2D pair distance, path-loss exponent, and fading parameter m. Finally, the results obtained from numerical analysis are corroborate through Monte-Carlo simulations which may gives the guidelines for network designer.
Optimizing Online Shopping using Genetic Algorithm

Recent advances in the technology has led to an exponential growth in the e-commerce business. Users today prefer Online Shopping to legacy shopping methods in order to save their effort and time. With the aim of attracting a greater number of customers, the online shopping portals offer products at cheaper rate than offline retailers. The increase in the number of online shopping portals are making it more difficult for the users to purchase a list of products at minimum cost. This can be attributed to the fact that different shopping portals offer different prices for the same product. This problem of obtaining a sequence of products to be procured from different shopping portals with the aim of minimizing the total cost has been termed as the Internet Shopping Optimization Problem. Owing to the NP-hardness of the problem, this paper proposes the application of Genetic Algorithm to solve the Internet Shopping Optimization Problem. Results obtained clearly indicates the efficiency of the proposed work in achieving the optimal solutions in polynomial time.
Ecology of Ectomycorrhizal‐Basidiomycete Communities on a Local Vegetation Gradient

To understand the factors that structure ectomycorrhizal—basidiomycete communities at a local scale, we measured the strength of the relations among the fungal communities, the tree communities, and the environment of a series of forest ecosystems in southern Quebec. We collected fruit bodies belonging to ectomycorrhizal—basidiomycete families and genera, sampled the woody vegetation, and described soils and landforms at 11 permanent sampling stations. We first calculated similarity matrices among stations, one for each descriptor (fungal species abundance, woody species abundance, and abiotic variables). We then explored the dependence among these matrices using Mantel and partial Mantel tests, path analysis, and comparisons of ordinations and classifications. Similarity among ectomycorrhizal fungus communities was strongly and significantly correlated with tree community similarity, even when controlling for the effect of environmental similarity. When the tests were made with a similarity matrix based on those tree species that are known to be hosts of ectomycorrhizal fungi, abiotic similarity explained a significant portion of the residual variation in the similarity among fungus communities. To explore this complex relationship further, we analyzed species associations. The preference of fungus associations for different sets of abiotic conditions showed that some factors affecting fungal species distribution were different from those affecting the distribution of their tree hosts. Direct and indirect gradient analyses showed that humus characteristics seemed to be important niche dimensions of ectomycorrhizal fungi. The continuum concept was useful to interpret the complex relations among symbiotic species. Trees were the main component of the realized niche of ectomycorrhizal Basidiomycetes, but the fungal symbionts of a particular tree species followed that tree species for only a part of the abiotic gradients over which the host tree was found. This type of distribution predicts that beta diversity of fungi would be generally higher than beta diversity of ectomycorrhizae—forming trees. It also means that the ratio of fungus species richness to woody species richness would be high for most community gradients. Our results and those of previous mycosynecological studies agree with these predictions. The results have implications for the conservation of biodiversity: site selection for conservation based on vegetation classification or mapping, or on distribution of tree species, may miss important fungal species.
Rendering Speed Improvement for Particle Based Fluid Simulation on GVDB Voxels using Whitted Raytracing

Fluid simulation is the representation of fluids and its displacement as in the real world on digital media such as computers. This research specifically discuss problem regarding visual rendering for fluid simulation. A common approach to represent fluid simulation is through groups of point representing volumetric fluid particles. This data representation approach is called as point cloud data. GVDB Voxels is a new framework that may aid storing, calculating, and rendering of fluid simulation data stored with the framework. In this research, it is specifically discussed techniques to render fluids stored in GVDB Voxels. This research explains how point cloud data is stored in GVDB Voxels, converted into level set data, rendered to images using raytracing, and lastly modified to perform trade-off between render speed and render quality. Raytracing was performed using NVIDIA OptiX Engine integration with GVDB Voxels. Using OptiX Engine, we implement a Whitted Ray tracer as alternative to the provided Monte Carlo Path Tracer. Moreover, we propose Quincunx antialiasing method to deal with appearing aliasing problem. Rendering with the proposed solution is able to reach realtime performance with FPS count 30.47 and higher SNR (signal-to-noise) value than the provided Monte Carlo Path Tracing method in particular cases. Future work related with this topic should aim to modify secondary ray tracer by incorporating depth bounding which may generate for FPS gains.
Switching at flit level: A Congestion Efficient Flow Control Strategy for Network-on-Chip

The proposed work demonstrates a novel flow control mechanism that brings the flexibility to route every flit of a packet independently, alleviating the network congestion more efficiently in Virtual Channel (VC) based NoC routing. A 100% buffer utilization is achieved by putting an end to the overhead of VC arbitration. The modified router design comes with a 20% area and 4.73% energy saving on the generic one. Packet Latency improvement of 35 -78 %, 27 - 68%, and 45 - 62% is observed over the mesh, torus, and flattened butterfly topologies respectively under uniform random traffic. The average packet drop rate is reduced by a minimum of 28% across the above topologies.
High light enhances the expression of low-CO2-inducible transcripts involved in the CO2-concentrating mechanism in Synechocystis sp. PCC6803

The relative abundances of key gene transcripts encoding proteins involved in inducible inorganic carbon (C i ) transport in the cyanobacterium Synechocystis PCC6803 were quantified by real-time reverse-transcriptase-polymerase chain reaction under conditions of varying C i and light levels. Significant increases in CO 2 -concentrating mechanism (CCM)-related transcript abundance were observed only in cells aerated with CO 2 -free air for 30 min in the light, but not in the dark, relative to illuminated cells grown at high CO 2 levels (high-C i cells). Cells were incubated under precisely defined conditions in a cuvette attached to a mass spectrometer to allow for the measurement of photosynthetic gas exchange rates, [C i ] and [O 2 ], in combination with quantitative analysis of transcripts. Under conditions of increasing irradiance and low [C i ], or under conditions of decreasing [C i ] at constant irradiance, the abundances of cmpA (encoding part of the BCT1 HCO 3 - transporter), ndhF3 (encoding subunit of high-affinity CO 2 uptake system) and sbtA (encoding Na + -dependent HCO 3 - transporter) transcripts tended to increase, relative to illuminated cells grown at high-CO 2 . The cmpA transcript appeared to be less responsive to decreasing [C;] than either ndhF3 or sbtA. The induction of cmpA and ndhF3 transcripts was completely inhibited by 10 μM 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) compared to untreated control cells. This inhibitor had no effect on sbtA expression. in the presence of 2,6-dichlorobenzoquinone (DCBQ), the expression of the cmpA transcript tracked the apparent rate of O 2 evolution from photosystem II closely as the irradiance was increased, reaching maximum levels of expression approximately 16-fold higher than control cells. Under the same conditions, the ndhF3 transcript increased by two- to three-fold, whereas the sbtA transcript did not respond to this treatment. The regulation of CCM induction in this strain is discussed in relation to current hypotheses on the sensing of C, limitation.
Very Low Phase Noise Voltage Controlled Oscillator for 5G mm-wave Communication Systems

A customized structure of the Colpitts VCO for the 5G millimeter band is presented in this paper, this structure has a very low phase noise level compared to other structures published in the literature. Simulation results show that our VCO has a tuning range of 1.69 GHz around 28 GHz, an output power of 3.3 dBm and a phase noise around -123.2dBc/Hz at 1 MHz offset frequency from carrier. The VCO is designed in MMIC technology using the pHEMT transistor of PH15 process from UMS foundry.
Hdconfigor: Automatically Tuning High Dimensional Configuration Parameters for Log Search Engines

Search engines are nowadays widely applied to store and analyze logs generated by large-scale distributed systems. To adapt to various workload scenarios, log search engines such as Elasticsearch usually expose a large number of performance-related configuration parameters. As manual configuring is time consuming and labor intensive, automatically tuning configuration parameters to optimize performance has been an urgent need. However, it is challenging because: 1) Due to the complex implementation, the relationship between performance and configuration parameters is difficult to model and thus the objective function is actually a black box; 2) In addition to application parameters, JVM and kernel parameters are also closely related to the performance and together they construct a high dimensional configuration space; 3) To iteratively search for the best configuration, a tool is necessary to automatically deploy the newly generated configuration and launch tests to measure the corresponding performance. To address these challenges, this paper designs and implements HDConfigor, an automatic holistic configuration parameter tuning tool for log search engines. In order to solve the high dimensional optimization problem, we propose a modified Random EMbedding Bayesian Optimization algorithm (mREMBO) in HDConfigor which is a black-box approach. Instead of directly using a black-box optimization algorithm such as Bayesian optimization (BO), mREMBO first generates a lower dimensional embedded space through introducing a random embedding matrix and then performs BO in this embedded space. Therefore, HDConfigor is able to find a competitive configuration automatically and quickly. We evaluate HDConfigor in an Elasticsearch cluster with different workload scenarios. Experimental results show that compared with the default configuration, the best relative median indexing results achieved by mREMBO can reach $2.07\times $ . In addition, under the same number of trials, mREMBO is able to find a configuration with at least a further 10.31% improvement in throughput compared to Random search, Simulated Annealing and BO.
Transductive Nonnegative Matrix Tri-Factorization

Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields. However, standard NMF algorithms ignore the training labels as well as unlabeled data in the test domain. In this paper, we propose a transductive nonnegative matrix tri-factorization method (T-NMTF) to simultaneously exploit the label information of training examples and the statistical structure of features in the test domain. Different from standard NMF, nonnegative matrix tri-factorization (NMTF) decomposes a nonnegative matrix into the product of three lower-rank nonnegative matrices, and thus provides a flexible framework to transduce discriminative information of training examples to test examples. In particular, the proposed T-NMTF projects both training examples and test examples into a unified subspace, and expects the coefficients of training examples close to their label vectors. Since training examples and test examples are assumed to identically distributed, it is reasonable to expect the learned coefficients of test examples approximate their label vectors well. To estimate the T-NMTF parameters, we develop an efficient multiplicative update rule and prove its convergence. In addition, we propose a manifold regularized T-NMTF (MT-NMTF) algorithm that exploits the local geometry structure of the dataset to boost discriminant power. Experimental results on face recognition demonstrate the effectiveness of T-NMTF and MT-NMTF.
3D Motion Estimation and Compensation Method for Video-Based Point Cloud Compression

A point cloud visualizes information by placing a voxel with a color value and a position value in a three-dimensional space. Since a point cloud uses hundreds of thousands or millions of points to visualize information, a large number of bits is needed compared to existing 2D media. Therefore, it is essential to compress point data for transmission and storage. The Moving Picture Expert Group (MPEG) is developing a point cloud compression method based on 2D video that takes advantage of the benefits of coding efficiency and the wide adaption of video codecs by various industries. This compression method is called video-based point cloud compression (V-PCC). Generally, video codecs use a compression method that employs a block matching algorithm. Currently, V-PCC is conducted using 2D video codecs, which means that motion information used by V-PCC is obtained from 2D video sequences. Thus, this 2D-based motion information limits the characterization of the motion in terms of 3D-points, which is also disadvantageous to compression efficiency. In this paper, we propose a method for estimating and compensating the motion in terms of a 3D object when compressing a dynamic object point cloud using a conventional video codec. The proposed 3D motion estimation and compensation technology showed higher gain overall in terms of BD-rate and was proven to effectively compress 3D point cloud content on the basis of 3D motion.
Glucose oxidase effects on gluten and water solubles

ABSTRACT Hydrogen peroxide was responsible for the improving the effect of glucose oxidase in breadmaking. The mechanism by which H2O2 has its effect is not known. The objective of this study was to determine whether the H2O2 produced by glucose oxidase affected the gluten proteins or the water-soluble fraction of flour. Glucose oxidase had no effect on gluten protein as measured by protein solubility or the relative viscosity of soluble protein (solubilized using 1.5% w/v SDS). However, glucose oxidase did affect the water-soluble fraction. The sulfhydryl content of the water-soluble fraction extracted from flour or dough decreased in the presence of glucose oxidase. Glucose oxidase also caused oxidative gelation of the water-soluble fraction extracted from flour. However, the viscosity of the water-soluble fraction extracted from fermented doughs containing glucose oxidase decreased when higher levels of glucose oxidase were used (≥5.0 units of glucose oxidase). Glucose oxidase appeared to have the same.
Towards well-founded extractions for large information diffusion on social networks

Social media platforms have exponentially grown over the past decade. Social networks platforms are used by one-in-three people on the planet, and more than two-thirds of all internet users. This explosive ascending number of users consequently lead to a growth in research of information analysis fields. The purpose of our study is to develop a method that helps propagate information efficiently throughout the social network. To manage this, it is crucial to evaluate user profiles accordingly to their shared content. The principle behind our method is that a user is more likely to share an information, if they are used to sharing content semantically related to its concept. In order to achieve this, this paper focuses on developing an algorithm, which selects seed nodes that are most likely to spread the word across the given social network. The key aspect of our method is to analyze textual content provided within a defined closed social network, while taking into consideration the relevancy aging time of content within the network. For the purpose of this analysis and given the random nature of posts shared by users, we develop our own NED (Name Entity Disambiguation) method, and UWI (Unusual Word Identification) method. We also create a semantic similarity method, in order to match an information with the right users.
Deep Learning-Based Illumination Estimation Using Light Source Classification

Color constancy is one of the key steps in the process of image formation in digital cameras. Its goal is to process the image so that there is no influence of illumination color on the colors of objects and surfaces. To capture the target scene colors as accurately as possible, it is crucial to estimate the illumination vector with high accuracy. Unfortunately, the illumination estimation is an ill-posed problem, and solving it most often relies on assumptions. To date, various assumptions have been proposed, which resulted in a wide variety of illumination estimation methods. Statistics-based methods have shown to be appropriate for hardware implementation, but learning-based methods achieve state-of-the-art results, especially those that use deep neural networks. The large learning capacities and generalization abilities of deep neural networks can be used to develop the illumination estimation methods, which are more general and precise. This approach avoids introducing many new assumptions, which often only work in some specific situations. In this paper, a new method for illumination estimation based on light source classification is proposed. In the first step, the set of possible illuminations is reduced by classifying the input image in one of three classes. The classes include images captured in outdoor scenes under natural illuminations, images captured in outdoor scenes under artificial illuminations, and images captured in indoor scenes under artificial illuminations. In the second step, a deep illumination estimation network, which is trained exclusively on images in the class that was predicted in the first step, is applied to the input image. Dividing the illumination space into smaller regions makes the training of illumination estimation networks simpler because the distribution of image scenes and illuminations is less diverse. The experiments on the Cube+ image dataset have shown the median illumination estimation error of 1.27°, which is an improvement of more than 25% compared to the use of the single network for all illuminations.