Miracle-dz/UniFAR-FTUs huggingface dataset:
OsWRKY10 extensively activates multiple rice diterpenoid phytoalexin biosynthesis to enhance rice blast resistance.

Phytoalexin is the main chemical weapon against disease pathogens in plants. Rice produces a number of phytoalexins to defend pathogens, most of which belong to diterpenoid phytoalexins. Three biosynthetic gene clusters (BGCs) and a few non-cluster genes are responsible for rice diterpenoid phytoalexin biosynthesis. The corresponding regulatory mechanism of these phytoalexins in response to pathogen challenges still remains unclear. Here we identified a transcription factor, OsWRKY10, positively regulating rice diterpenoid phytoalexin biosynthesis. Knockout mutants of OsWRKY10 obtained by the CRISPR/Cas9 technology are more susceptible to Magnaporthe oryzae infection, while overexpression of OsWRKY10 enhances resistance to rice blast. Further analysis reveals that overexpression of OsWRKY10 increases accumulation of multiple rice diterpenoid phytoalexins and expression of genes in three BGCs and non-clustered genes in response to M. oryzae infection. Knockout of OsWRKY10 impairs upregulation of rice diterpenoid phytoalexin biosynthesis gene expression by blast pathogen and CuCl2 treatment. OsWRKY10 directly binds to the W-boxes or W-box-like elements (WLEs) of rice diterpenoid phytoalexin biosynthesis gene promoters to regulate the corresponding gene expression. This study identified an extensive regulator (OsWRKY10) with the broad transcriptional regulation on rice diterpenoid phytoalexin biosynthesis, providing the insight to characterize regulation of rice chemical defense for improving disease resistance.
NON-LOCAL MODELS FOR DIFFUSION IN ATMOSPHERIC CALM

Summary The non-local diffusion equation in space and time previously determined is applied to the particular case of diffusion in calm. Non-local diffusion models are presented in relation to the non-markovian character of turbulent diffusion. A space-time non-local onedimensional model is used to show that non-local models are consistent with the statistical theory of turbulent diffusion, even without the introduction of virtual diffusion coefficients (K). A series of non-local models for continuous and instantaneous sources are derived and the consequences of these models compared to K-theory are discussed.
Urban public space quality evaluation methods and practices in China

As the focus of human settlements in China shifts from incremental to quality improvement, scientific assessment and quantitative research on urban public space quality are urgently needed. However, there is no research to review and summarize the progress of urban public space quality evaluation of Chinese publications. Through a systematic review, this study screens the relevant literature of China National Knowledge Infrastructure and conducts statistical analysis on the 43 included studies, including the publication time, authors’ field, study area, study dimension, index system, method and purpose. It is found that Chinese studies on urban public space quality evaluation have developed rapidly in the last five years, and the study objects mainly focus on street space. A few studies (17, 32.6%) can focus on both physical space characteristics and human perception. Compared with traditional methods such as questionnaire survey and field survey, street view images combined with machine learning technology is now the most commonly used method to measure public space quality. The purposes of the existing studies mainly focus on the space quality evaluation itself (22, 51.2%), and the optimization of measurement methods and the externalities, influencing factors and mechanisms of public space quality need to be further studied.
Fluid–structure coupled response of dynamical surfaces tailored in a flexible shell

The present study examines the fluid-coupled wave response of a stepped elastic membrane discontinuity connecting a flexible shell to the annular rigid shell. The acoustic radiation from the internal and annular regions propagates along the flexible boundaries as well as through the compressible fluid that fills the interior of the shell and scatters at geometric discontinuity and edges. In contrast to previous studies, an elastic membrane disc with different boundary conditions is considered as the geometric discontinuity rather than an acoustically rigid step-discontinuity in a cylindrical waveguide. The problem governed by the rigid step-discontinuity is solved using the mode matching technique, while the Galerkin procedure is used for the elastic membrane discontinuity. The truncated form of both solutions reconstructs the matching conditions and satisfies the conservation laws, which further authenticate the correctness of the solutions and the validity of the obtained numerical results based on these solutions. It is worth noting that the parametric configuration of the elastic membranes, the edge conditions and the radii of the shells have a significant impact on the attenuation of the noise control device.
Angiotensin-converting enzyme insertion/deletion gene polymorphism and Henoch-Schonlein purpura nephritis risk in children: a meta-analysis.

It has been demonstrated in many studies that angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism is related to Henoch-Schonlein purpura nephritis (HSPN) risk in children. However, this conclusion remains controversial. In this study, we systemically retrieved relevant studies in electronic databases such as PUBMED, CNKI, and EMBASE followed by calculation of odds ratios (OR) with 95% confidence intervals (CI). In addition, meta-package in STATA version 12.0 was used. Angiotensin-converting enzyme I/D polymorphism was related to HSPN susceptibility in children (D vs. I: OR 1.47, 95% CI: 1.13-1.93; DD vs. II: OR 2.29, 95% CI: 1.29-4.07; DI vs. II: OR 1.10, 95% CI: 0.82-1.48; dominant model: OR 1.44, 95% CI: 1.09-1.89; recessive model: OR 2.26, 95% CI: 1.67-3.06). In addition, subgroup analysis stratified according to ethnicity indicated a significant relationship between this polymorphism and HSPN susceptibility among Asians and Caucasians. The data extracted from HaploReg indicated that ACE I/D polymorphism was not in linkage disequilibrium with other variants in the ACE gene. The research shows that ACE I/D polymorphism is related to HSPN susceptibility in children.
PENGARUH GAYA KEPEMIMPINAN, KEMAMPUAN DAN MOTIVASI KERJA TERHADAP KINERJA KARYAWAN BANK PEMBIAYAAN RAKYAT SYARIAH (BPRS) BHAKTI SUMEKAR SUMENEP

ABSTRACT This research was conducted at the Sharia Community Financing Bank (BPRS) Bhakti Sumekar Sumenep with a total sample of 60 people (sampling technique was random sampling). The purposes of this study are: 1) To describe the leadership style, ability, work motivation and performance of employees, 2) To test and analyze whether the leadership style, ability and work motivation simultaneously have a significant influence on the performance of employees 3) To test and analyze whether leadership style, ability and work motivation partially have a significant influence on the performance of employees. Analysis of data using SPSS, this type of research is explanatory research using quantitative research approaches. The results of the study show that: 1) Simultaneously the leadership style, ability and motivation of work have an influence on the performance of employees, 2) Partially the leadership style, ability and motivation of work have an influence on the performance of employees. Keywords: Leadership Style (X1), Ability (X2), Motivation (X3) And Work Performance Of Employees (Y).
Minimum spanning tree-based digital terrain model detection from light detection and ranging points

LiDAR is widely used to collect point data-set representing the scanning scenes. Several products can be extracted from raw LiDAR data. DTM is among the most important. For traditional DTM detection approaches such as TIN-based and parametric methods, a negative outlier detector is employed beforehand since negative outliers will drive these methods to converge into an erroneous terrain surface. However, automatic outlier removal remains challenging in related fields. To overcome the obstacle, this article proposes minimum spanning tree-based detection method. An undirected graph is constructed for LiDAR points on basis of delauney rule. Edges are weighted by absolute slope value. Then, greedy algorithm is taken to generate MSF for the undirected graph. Edges with steep slopes are omitted in this process, which is how more than one MST arises. The DTM is generally the MST with largest area. Cases validate the effectiveness.
Detection of defective areas and hidden weak patterns in the wafer using massive measurement data

As the measurability (sampling capacity, measurement coverage, and measurement speed) of metrology systems are being enhanced to keep pace with the evolution of semiconductor manufacturing processes, the detection of defective areas and hidden weak patterns by analyzing the massive measurement data is becoming significantly important. In this study, we propose new methods to detect defective areas and hidden weak patterns by mathematically processing massive measurement data. By applying the methods we propose, we were able to successfully detect the hidden weak signals of the millimeter scale in the wafer.
Acinetobacter baumannii under Acidic Conditions Induces Colistin Resistance through PmrAB Activation and Lipid A Modification

Colistin is a last-resort antimicrobial agent for treating carbapenem-resistant Acinetobacter baumannii infections. The activation of PmrAB by several environmental signals induces colistin resistance in Gram-negative bacteria. This study investigated the molecular mechanisms of colistin resistance in A. baumannii under acidic conditions using wild-type (WT) A. baumannii 17978, ΔpmrA and ΔpmrB mutants, and pmrA-complemented strains. The pmrA or pmrB deletion did not affect the growth of A. baumannii under acidic or aerobic conditions. A. baumannii under acidic (pH 5.5) and high-iron (1 mM) conditions showed 32- and 8-fold increases in the minimum inhibitory concentrations (MICs) of colistin, respectively. The ΔpmrA and ΔpmrB mutants at pH 5.5 showed a significant decrease in colistin MICs compared to the WT strain at pH 5.5. No difference in colistin MICs was observed between WT and mutant strains under high-iron conditions. The pmrCAB expression significantly increased in the WT strain at pH 5.5 compared to the WT strain at pH 7.0. The pmrC expression significantly decreased in two mutant strains at pH 5.5 compared to the WT strain at pH 5.5. The PmrA protein was expressed in the ΔpmrA strain carrying ppmrA_FLAG plasmids at pH 5.5 but not at pH 7.0. Lipid A modification by the addition of phosphoethanolamine was observed in the WT strain at pH 5.5. In conclusion, this study demonstrated that A. baumannii under acidic conditions induces colistin resistance via the activation of pmrCAB operon and subsequent lipid A modification.
An adaptive estimation of dimension reduction space

Summary. Searching for an effective dimension reduction space is an important problem in regression, especially for high dimensional data. We propose an adaptive approach based on semiparametric models, which we call the (conditional) minimum average variance estimation (MAVE) method, within quite a general setting. The MAVE method has the following advantages. Most existing methods must undersmooth the nonparametric link function estimator to achieve a faster rate of consistency for the estimator of the parameters (than for that of the nonparametric function). In contrast, a faster consistency rate can be achieved by the MAVE method even without undersmoothing the nonparametric link function estimator. The MAVE method is applicable to a wide range of models, with fewer restrictions on the distribution of the covariates, to the extent that even time series can be included. Because of the faster rate of consistency for the parameter estimators, it is possible for us to estimate the dimension of the space consistently. The relationship of the MAVE method with other methods is also investigated. In particular, a simple outer product gradient estimator is proposed as an initial estimator. In addition to theoretical results, we demonstrate the efficacy of the MAVE method for high dimensional data sets through simulation. Two real data sets are analysed by using the MAVE approach.
Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset

Low-noise CD-SEM images are required in order to obtain robust and reliable measurement results, especially for complex 2D patterns. However, standard practices to reduce CD-SEM noise during image acquisition (increasing number of frames, increasing beam current, etc.) also increase acquisition time and the probability of deteriorating the materials under inspection. This effect is getting further attention of the industry on the case of EUV (extreme ultraviolet) resist, being an electron sensitive material and presenting small thickness, requiring extra care in order to prevent damage. Although there are techniques that intrinsically improve the metrology robustness to noise (such as model-based contour extraction), denoising SEM images may prove useful and a complementary approach for further improve metrology quality in such challenging cases. In this work we propose an innovative solution using Deep Learning Neural Network (DLNN) for low frame image denoising, which performs the model calibration using low frame images only, from a reduced dataset. A specific data augmentation approach is used in order to limit the number of images needed for the training. The denoising performance of the algorithm was evaluated in terms of accuracy and precision over a synthetic dataset, not used during the training of the neural network. The results show an improvement both in precision (up to 50% on the extreme case, 5% on average) as well in accuracy (over 13% on average).
Symbiotic microbiota vary with breeding group membership in a highly social joint-nesting bird

Symbiotic microbes affect the health, fitness, and behavior of their animal hosts, and can even affect the behavior of non-hosts. Living in groups presents numerous benefits and challenges to social animals, including exposure to symbiotic microbes, which can mediate both cooperation and competition. In social mammals, individuals from the same social group tend to share more similar microbes and this social microbiome, the microbial community of all hosts in the same social group, can shape the benefits and costs of group living. In contrast, little is known about the social microbiome of group living birds. We tested the predictions that communally breeding smooth-billed anis (Crotophaga ani) belonging to the same breeding group share more similar microbes and that microbial community composition differs between body regions. To test this, we used 16S rRNA gene sequencing to characterize the preen gland and body feather microbiota of adult birds from 16 breeding groups at a long-term study site in southwestern Puerto Rico. As predicted, individuals from the same breeding group shared more similar microbiota than non-group members and preen gland and body feathers harbored distinct microbial communities. Future research will evaluate whether this social microbiome affects the behavior of group living birds.
RAD51 Inhibition Shows Antitumor Activity in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the major type of liver cancer, causes a high annual mortality worldwide. RAD51 is the critical recombinase responsible for homologous recombination (HR) repair in DNA damage. In this study, we identified that RAD51 was upregulated in HCC and that RAD51 silencing or inhibition reduced the proliferation, migration, and invasion of HCC cells and enhanced cell apoptosis and DNA damage. HCC cells with the combinatorial treatments of RAD51 siRNA or inhibitor and sorafenib demonstrated a synergistic effect in inhibiting HCC cell proliferation, migration, and invasion, as well as inducing cell apoptosis and DNA damage. Single RAD51 silencing or sorafenib reduced RAD51 protein expression and weakened HR efficiency, and their combination almost eliminated RAD51 protein expression and inhibited HR efficiency further. An in vivo tumor model confirmed the RAD51 inhibitor’s antitumor activity and synergistic antitumor activity with sorafenib in HCC. RNA-Seq and gene set enrichment analysis (GSEA) in RAD51-inactivated Huh7 cells indicated that RAD51 knockdown upregulated cell apoptosis and G1/S DNA damage checkpoint pathways while downregulating mitotic spindle and homologous recombination pathways. Our findings suggest that RAD51 inhibition exhibits antitumor activities in HCC and synergizes with sorafenib. Targeting RAD51 may provide a novel therapeutic approach in HCC.
Integrated visual vocabulary in latent Dirichlet allocation–based scene classification for IKONOS image

Abstract Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼ 2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼ 20 % .
Legal Information Retrieval Based on a Concept-Frequency Representation and Thesaurus

: The retrieval of legal information has become one of the main topics in the legal domain, which is characterized by a huge amount of digital documents with a peculiar language. This paper presents a novel approach, called BoLC-Th (Bag of Legal Concepts Based on Thesaurus), to represent legal texts based on the Bag-of-Concept (BoC) approach. The novel contribution of the BoLC-Th is to generate weighted histograms of concepts defined from the distance of the word to its respective similar term within a thesaurus. This approach allows to emphasize those words that have more significance for the context, thus generating more discriminative vectors. We performed experimental evaluations by comparing the proposed approach with the traditional Bag-of-Words (BoW), TF-IDF and BoC approaches, which are popular techniques for document representation. The proposed method obtained the best result among the evaluated techniques for retrieving judgments and jurisprudence documents. The BoLC-Th increased the mAP (mean Average Precision) compared to the traditional BoC approach, while being faster than the traditional BoW and TF-IDF representations. The proposed approach contributes to enrich a domain area with peculiar characteristics, providing a resource for retrieving textual information more accurately and quickly than other techniques based on natural language processing.
Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

Energy efficiency is one of the critical parameters affecting production in the natural stone sector, as it is in every industrial sector. High energy consumption negatively affects production costs, especially in stone cutting and surface treatments. Nowadays, it is crucial to predetermine energy consumption with reliable predictive techniques to produce with the lowest energy possible and sustain sectorial competition. This study conducted stone cutting tests with a computer-assisted circular cutting machine at different peripheral speeds (PSs) and advance rates (ARs). Unit energy (UE) consumptions were measured in stone cuttings. UE was evaluated regarding the circular stone cutting machine’s (CSCM's) operating parameters, some stone characteristics, vibration amplitude (VA), and sound level (SL) measured during cutting. Classical statistics (CS) and data mining (DM) techniques were used to predict UE. 287 and 24 cutting data sets were selected as training and testing data for CS and DM techniques, respectively. These techniques were also compared and provided more significant and reliable results of DM techniques than CS. DM techniques predicted the UE with high correlation coefficients obtained in the range of R 2 =0.963 and 0.973. DM models for UE prediction before stone cutting have been introduced for stone processing researchers and those interested.
Automated Detection of Heart Valve Diseases Using Stationary Wavelet Transform and Attention-Based Hierarchical LSTM Network

This article presents a phonocardiogram (PCG)-based heart valve disease (HVD) detection for primary healthcare units. In this work, we propose a stationary wavelet transform (SWT) decomposition, followed by an attention-based hierarchical long short-term memory (HLSTM) network for each subband to detect HVDs. Initially, the PCG signal is preprocessed and segmented into PCG segments. Then, each PCG segment is decomposed using SWT. We employ subband-specific HLSTM networks to utilize the temporal and relative temporal information present in each subband of the PCG segment for detecting HVDs. Then, the outputs of each subband-specific HLSTM are fed to their respective intra-subband attention layer for weighted aggregation to obtain the subband-specific representation. Furthermore, the inter-subband attention layer aggregates these subband-specific representation vectors to improve the detection of HVDs. The proposed method is tested and validated using two open-access databases. The Physionet Challenge 2016 database shows an overall sensitivity (OSe) of 97.96%, overall specificity (OSp) of 99.02%, and overall accuracy (OA) of 98.55%, using the proposed method for binary classification. Similarly, the heart sound (HS) murmur database shows impressive classwise performance measures and an OA of 99.47%, using the proposed method for multiclass HVD classification. The proposed method’s impressive performance and generalization can help to detect HVD anomalies during preliminary checkups in healthcare units.
Origin of the Coulomb pseudopotential

We address the outstanding problem of electron pairing in the presence of strong Coulomb repulsion at small to moderate values of the Coulomb parameter, $r_s \lesssim 2$, and demonstrate that the pseudopotential framework is fundamentally biased and uncontrolled. Instead, one has to break the net result into two distinctively different effects: the Fermi liquid renormalization factor and the change in the effective low-energy coupling. The latter quantity is shown to behave non-monotonically with an extremum at $ r_s\approx 0.75$. Within the random-phase approximation, Coulomb interaction starts to enhance the effective pairing coupling at $r_s>2$, and the suppression of the critical temperature is entirely due to the renormalized Fermi liquid properties. Leading vertex corrections change this picture only quantitatively. Our results call for radical reconsideration of the widely accepted repulsive pseudopotential approach and show the need for precise microscopic treatment of Coulomb interactions in the problem of superconducting instability.