Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Gene Expression in Soybean Leaf Tissues at Late Developmental Stages under Drought Stress Revealed by Genome-Wide Transcriptome Analysis

The availability of complete genome sequence of soybean has allowed research community to design the 66 K Affymetrix Soybean Array GeneChip for genome-wide expression profiling of soybean. In this study, we carried out microarray analysis of leaf tissues of soybean plants, which were subjected to drought stress from late vegetative V6 and from full bloom reproductive R2 stages. Our data analyses showed that out of 46093 soybean genes, which were predicted with high confidence among approximately 66000 putative genes, 41059 genes could be assigned with a known function. Using the criteria of a ratio change > = 2 and a q-value<0.05, we identified 1458 and 1818 upregulated and 1582 and 1688 downregulated genes in drought-stressed V6 and R2 leaves, respectively. These datasets were classified into 19 most abundant biological categories with similar proportions. There were only 612 and 463 genes that were overlapped among the upregulated and downregulated genes, respectively, in both stages, suggesting that both conserved and unconserved pathways might be involved in regulation of drought response in different stages of plant development. A comparative expression analysis using our datasets and that of drought stressed Arabidopsis leaves revealed the existence of both conserved and species-specific mechanisms that regulate drought responses. Many upregulated genes encode either regulatory proteins, such as transcription factors, including those with high homology to Arabidopsis DREB, NAC, AREB and ZAT/STZ transcription factors, kinases and two-component system members, or functional proteins, e.g. late embryogenesis-abundant proteins, glycosyltransferases, glycoside hydrolases, defensins and glyoxalase I family proteins. A detailed analysis of the GmNAC family and the hormone-related gene category showed that expression of many GmNAC and hormone-related genes was altered by drought in V6 and/or R2 leaves. Additionally, the downregulation of many photosynthesis-related genes, which contribute to growth retardation under drought stress, may serve as an adaptive mechanism for plant survival. This study has identified excellent drought-responsive candidate genes for in-depth characterization and future development of improved drought-tolerant transgenic soybeans.
Discovering Significant Evolution Patterns from Satellite Image Time Series

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.
Optimization of Cluster Analysis Using Autocorrelation

Most current methods for measuring the number of clusters use optimum index values by comparing within-group and between-group variations at a global scale. An indicator based on local spatial variation to decide the existence of local scale cluster is needed. This study employed a new k-means algorithm for cluster analyses and geostatistical variograms for spatial autocorrelation. The criteria to optimize the numbers of clusters using global/local scale autocorrelation were discussed. Average annual temperature and precipitation from 1971 to 2000 at 350 weather stations throughout the State of Texas were used in two analyses. The first one investigated the relationship between cluster analysis and autocorrelation for a single variable-average spring temperature. Another analyzed multi-variant cluster and autocorrelation for average annual temperature and precipitation. The results suggest that global and local variograms can be used to indicate if further partitioning is needed within each cluster and in turn help to optimize the number of clusters. Future research is needed for the criteria to decide if the poorly fitted autocorrelation function data is due to the results of combination of smaller scales variograms or plain noises.
Detection of inferior myocardial infarction using shallow convolutional neural networks

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.
Feature Reduction Using Ensemble Approach

The performance of many content analysis methods heavily dependent on the features they are applied. A fundamental problem that makes the content analysis difficult is the curse of dimensionality. In this study, we propose a novel feature reduction method which adopts ensemble approach to measure the divergence between the training set and test set and use the divergence to supervise the feature reduction procedure. The proposed method uses pairwise measure to get the diversity between classifiers and selects the complementary classifiers to get the pseudo labels on test set. The pseudo labels are used to measure the divergence between training set and test set. The feature reduction algorithm merges the adjacent feature space according to the divergence, such reduce the feature number. We evaluated the proposed method on several standard datasets. Experiment results shown the efficiency of the proposed feature reduction method.
Fluorescence LIDAR system modeling for underwater object recognition performance evaluation

This paper presents an end-to-end fluorescence LIDAR system modeling approach for predicting the performance of underwater object recognition. The overall LIDAR system chain is modeled, starting from LIDAR pulse generation up to collection and processing of the inelastic backscattering signal returned by the underwater object. A comprehensive simulator to reproduce in-water LIDAR propagation phenomena is included in the model. The proposed approach is capable of predicting underwater object recognition performance under various different fluorescence LIDAR system characteristics as well as viewing geometries and environmental conditions. Experimental results featuring immersion of an object in Case 1 and Case 2 waters show the potential of the proposed modeling approach.
A Test of Biological and Behavioral Explanations for Gender Differences in Telomere Length: The Multi-Ethnic Study of Atherosclerosis

The purpose of this study was to examine biological and behavioral explanations for gender differences in leukocyte telomere length (LTL), a biomarker of cell aging that has been hypothesized to contribute to women’s greater longevity. Data are from a subsample (n = 851) of the Multi-Ethnic Study of Atherosclerosis, a population-based study of women and men aged 45 to 84. Mediation models were used to examine study hypotheses. We found that women had longer LTL than men, but the gender difference was smaller at older ages. Gender differences in smoking and processed meat consumption partially mediated gender differences in telomere length, whereas gender differences in estradiol, total testosterone, oxidative stress, and body mass index did not. Neither behavioral nor biological factors explained why the gender difference in LTL was smaller at older ages. Longitudinal studies are needed to assess gender differences in the rate of change in LTL over time; to identify the biological, behavioral, and psychosocial factors that contribute to these differences throughout the life course; and to determine whether gender differences in LTL explain the gender gap in longevity.
Topological mapping: a dimensionality reduction method for efficient video search

In this paper, we present a novel approach for efficient search of shots by the colors of objects that exist in the wanted shots. The idea is to map the color histogram, which represents the color content, of each object in the shot from the high-dimensional feature space into a point in a low-dimensional distance space. Then, a spatial access method, such as an R-tree, is used to cluster these points based on their distances in the low-dimensional space. Our mapping method, called topological mapping, guarantees no false dismissals in the result of a query. However, the result of a query might contain some false alarms. Hence, two refinement steps are performed to remove these false alarms. Comparative experiments show the superiority in efficiency of the topological mapping method over other methods.
Parent-implemented enhanced milieu teaching with preschool children who have intellectual disabilities.

PURPOSE The purpose of this study was to compare the effects of enhanced milieu teaching (EMT) implemented by parents and therapists versus therapists only on the language skills of preschool children with intellectual disabilities (IDs), including children with Down syndrome and children with autism spectrum disorders. METHOD Seventy-seven children were randomly assigned to 2 treatments (parent + therapist EMT or therapist-only EMT) and received 36 intervention sessions. Children were assessed before, immediately after, 6 months after, and 12 months after intervention. Separate linear regressions were conducted for each standardized and observational measure at each time point. RESULTS Parents in the parent + therapist group demonstrated greater use of EMT strategies at home than untrained parents in the therapist-only group, and these effects maintained over time. Effect sizes for observational measures ranged from d = 0.10 to d = 1.32 favoring the parent + therapist group, with the largest effect sizes found 12 months after intervention. CONCLUSION Findings from this study indicate generally that there are benefits to training parents to implement naturalistic language intervention strategies with preschool children who have ID and significant language impairments.
Expression of adhesion molecules in early rheumatoid synovial tissue.

The objective of this paper is to define the expression of adhesion molecules in synovial tissue (ST) from patients with rheumatoid arthritis (RA) with respect to disease duration. Antibodies against adhesion molecules were used for immunohistochemistry to examine ST sections from 11 patients with early RA ( < 1 year), 14 patients with long-standing RA ( > 5 years), and 15 patients with osteoarthritis (OA). Increased cellular infiltration and increased expression of E-selectin, ICAM-1, VCAM-1, PECAM-1, VLA-4, and Mac-1 were found in ST from patients with RA compared to ST from patients with OA. The immunohistological findings were similar for the different stages of RA. The upregulation of adhesion molecules in ST of patients with RA of < 1 year's duration suggests the activation of chronic inflammatory processes in these patients. Therefore, the mechanisms by which therapies directed toward these adhesion molecules exert their effects are likely to be similar for patients with so-called early RA and patients with long-standing RA.
A Self-Tuning Iterative Calculation Approach for the Forward Kinematics of a Stewart-Gough Platform

In order to solve the forward kinematics problem of a Stewart-Gough platform, we propose a self-tuning iterative calculation approach based on the inverse kinematics of the Stewart-Gough platform and the self-tuning method. The novelty of this approach is the use of two weighting factors, which are constructed via an exponential attenuation function by using the self-tuning method. With the employment of these two weighting factors, the number of iterative calculations is significantly reduced, and the efficiency of the proposed approach is thus substantially improved. Moreover, for convenience of implementation, the relationship between the values of the weighting factors and the number of iterative calculations is analyzed, and the values of the weighting factors are determined. Finally, simulation results demonstrate that the proposed approach can significantly improve calculation efficiency and reduce the time required for calculations compared with conventionally used iterative approaches
Acquisition of EM propagation parameters onboard trains at UHF frequencies

This paper reports numerical results for the characterization of the propagation channel in a train. Since the availability of a train to carry out measurements is not always easy, particularly when many changes must be done, a simulation tool provides a useful and reliable mean for the evaluation of the propagation characteristics of this complex and highly fluctuating channel. In order to benefit from previous results, the various existing softwares for complex electromagnetic fields environments simulations were fully searched and one that seems best suited has been retained for these computations. The results presented here are original, preliminaries and our approach provides a basis for study the propagation of waves in a very complex environment consisting of different electromagnetic fields like a train.
LogOptPlus: Learning to Optimize Logging in Catch and If Programming Constructs

Software logging is a common software development practice which is used to log program execution points. This execution information is later used by software developers for debugging purpose. Software logging is useful but it has cost and benefit tradeoff. Hence it is important to optimize the number of log statements in the code. However, previous studies on logging show that optimal logging is challenging for software developers. Hence tools and techniques which can help developers in making optimized logging decision can be beneficial. We propose LogOptPlus, a machine learning based tool to help software developers to optimize the number of log statements in the source code, for two focused code construct types. LogOptPlus is a significant extension of our previously published work 'LogOpt'. LogOpt is designed to predict logging for catch-blocks. We extend the functionality of LogOpt to predict logging for both if-blocks and catch-blocks. We identify distinguishing static features from the source code for logging prediction. We present intuition and results of quantitative analysis of all the features. We present results of comprehensive evaluation of LogOptPlus with five different machine learning algorithms on two two large Open Source projects (i.e., Apache Tomcat and CloudStack). Encouraging experimental results on two Open Source projects show that LogOptPlus is effective in logging prediction for two focused code construct types.
Speed and angle estimation of mobile terminal in distributed antenna systems

In the wireless mobile communications the knowledge of the mobile speed and/or the mobile velocity is of great importance because it is very helpful in the development of the system management algorithms (i.e. the handoff algorithms). Furthermore, the angle of mobile motion is more helpful for developing these algorithms and also in the mobile unit (MU) tracking systems. The proposed scheme showed that in distributed antenna system (DAS), the speed of MU at any point can be estimated using Doppler frequency estimation. The angle at which MU is traveling with respect to angle zero is derived in terms of Doppler frequency at two nearby locations. A numerical examples and comparison with test angles results were shown the accuracy of the estimation scheme within the real systems range of values.
Mode field expansion in index-guiding microstructured optical fibers

The mode-field expander (MFE) is a microstructured optical fiber (MOF) based device that enlarges the modal field distribution and can couple light from large mode area (LMA) fibers into small core fibers or vice-versa and other optical waveguides. Using our earlier developed analytical field model, we studied the mode-field expansion in MOFs having triangular lattice, and low-loss splicing of MOFs to standard single-mode fibers (SMFs), based on the controlled all airhole collapse method, which leads to an optimum mode-field match at the joint interface of the MOF-SMF. Comparisons with available experimental and simulation results have also been included.
SAMURAI: A batch and streaming context architecture for large-scale intelligent applications and environments

Over the past decade intelligent environments have grown in sophistication. Many recent paradigm shifts − such as the Internet of Things (IoT), Ambient Assisted Living (AAL), e-health and telemedicine − envision large distributed networks of intelligent devices, applications and services that are sensitive to the presence of people and responsive to their needs. Cutting edge technologies will autonomously and collectively operate on a growing volume of information arriving at ever increasing velocities to transparently and non-intrusively support users during their activities. Especially the escalating variety of information that applications have to deal with is a non-trivial concern. Making sense out of heterogeneous and pervasive streams of sensor events to anticipate and address the needs of users is a ubiquitous challenge that many interactive context-aware applications in intelligent environments frequently face. Furthermore, software solutions that continuously interpret the tasks and contexts of a variety of individuals with different needs are often faced with scalability concerns. We present SAMURAI, a batch and streaming context architecture that integrates and exposes well-known components for complex event processing, machine learning, and knowledge representation. SAMURAI builds upon key concepts of the Lambda architecture and big data enabling technologies to achieve horizontal scalability and responsive interaction with its users. Two application cases validate the feasibility and performance of our context architecture, demonstrating near-linear scalability, flexible elasticity and smooth interaction capabilities.
Adding Azithromycin to Cephalosporin for Cesarean Delivery Infection Prophylaxis: A Cost-Effectiveness Analysis.

OBJECTIVE To investigate the cost-effectiveness of adding azithromycin to standard cephalosporin regimens of cesarean delivery prophylaxis by considering the maternal outcomes in the current and potential subsequent pregnancies. METHODS A cost-effectiveness model was created using TreeAge to compare the outcomes of using azithromycin-cephalosporin with cephalosporin alone in a theoretical cohort of 700,000 women, the approximate number of nonelective cesarean deliveries annually in the United States that occur during labor or after membrane rupture. Outcomes examined included endometritis, wound infection, sepsis, venous thromboembolism, and maternal death in the current pregnancy and uterine rupture, cesarean hysterectomy, and maternal death in subsequent pregnancies, including cost and quality-adjusted life-years for both pregnancies. Probabilities, utilities, and costs were derived from the literature, and a cost-effectiveness threshold was set at $100,000 per quality-adjusted life-year. Sensitivity analyses were used to determine the robustness of our results. RESULTS Compared with cephalosporin alone for prophylaxis, our model showed 16,100 fewer cases of endometritis, 17 fewer cases of sepsis, eight fewer cases of venous thromboembolism, and one fewer maternal death with azithromycin-cephalosporin. Additionally, this strategy prevented 36 uterine ruptures and four cesarean hysterectomies in the subsequent pregnancy. Overall, the addition of azithromycin led to both lower costs and higher quality-adjusted life-years when compared with standard cephalosporin prophylaxis. In sensitivity analysis, we found that as long as the cost of azithromycin remained below $930 (baseline cost $27), it was cost-effective. CONCLUSION For women who undergo cesarean delivery in labor or after membrane rupture, compared with cephalosporin alone, the addition of azithromycin to cesarean delivery infection prophylaxis is less costly and leads to better maternal outcomes in the index delivery and subsequent deliveries. These findings support the use of prophylactic azithromycin at the time of cesarean delivery.
Duetting in space: a radio-telemetry study of the black-bellied wren

In many birds, individuals ‘answer’ the songs of their pair-mates to produce vocal ‘duets’. One hypothesized function of song answering is that it prevents extra-pair birds from intruding into the duetting pair's territory to obtain copulations or usurp one of the pair-mates. In this capacity, answering may signal that the pair-mates are close together, and so are prepared to defend against such an intrusion. Another functional hypothesis states that answering helps pair-mates maintain contact, and so predicts that a bird is more likely to approach its mate after a duet than after a solo song. I used radio-telemetry to monitor the distance between mated black-bellied wrens (Pheugopedius fasciatoventris). I found that birds of both sexes were more likely to answer their mate's song when the mate was close, and that maximum duet length was negatively related to the distance between pair-mates. Furthermore, song answering positively affected the likelihood of one pair-mate approaching the other after a song. In a significant majority of the approaches after duet songs, the answering bird approached the initiator. I conclude that in the black-bellied wren, (i) the occurrence and duration of vocal duets covary with physical closeness and (ii) contact maintenance is a secondary function of duet participation.