Miracle-dz/UniFAR-FTUs huggingface dataset:
Association Mapping of Lathyrus sativus Disease Response to Uromyces pisi Reveals Novel Loci Underlying Partial Resistance

Uromyces pisi ([Pers.] D.C.) Wint. is an important foliar biotrophic pathogen infecting grass pea (Lathyrus sativus L.), compromising their yield stability. To date, few efforts have been made to assess the natural variation in grass pea resistance and to identify the resistance loci operating against this pathogen, limiting its efficient breeding exploitation. To overcome this knowledge gap, the genetic architecture of grass pea resistance to U. pisi was investigated using a worldwide collection of 182 accessions through a genome-wide association approach. The response of the grass pea collection to rust infection under controlled conditions and at the seedling stage did not reveal any hypersensitive response but a continuous variation for disease severity, with the identification of promising sources of partial resistance. A panel of 5,651 high-quality single-nucleotide polymorphism (SNP) markers previously generated was used to test for SNP-trait associations, based on a mixed linear model accounting for population structure. We detected seven SNP markers significantly associated with U. pisi disease severity, suggesting that partial resistance is oligogenic. Six of the associated SNP markers were located in chromosomes 4 and 6, while the remaining SNP markers had no known chromosomal position. Through comparative mapping with the pea reference genome, a total of 19 candidate genes were proposed, encoding for leucine-rich repeat, NB-ARC domain, and TGA transcription factor family, among others. Results presented in this study provided information on the availability of partial resistance in grass pea germplasm and advanced our understanding of the molecular mechanisms of quantitative resistance to rust in grass pea. Moreover, the detected associated SNP markers constitute promising genomic targets for the development of molecular tools to assist disease resistance precision breeding.
Applied Bayesian Approaches for Research in Motor Neuron Disease

Statistical evaluation of empirical data is the basis of the modern scientific method. Available tools include various hypothesis tests for specific data structures, as well as methods that are used to quantify the uncertainty of an obtained result. Statistics are pivotal, but many misconceptions arise due to their complexity and difficult-to-acquire mathematical background. Even though most studies rely on a frequentist interpretation of statistical readouts, the application of Bayesian statistics has increased due to the availability of easy-to-use software suites and an increased outreach favouring this topic in the scientific community. Bayesian statistics take our prior knowledge together with the obtained data to express a degree of belief how likely a certain event is. Bayes factor hypothesis testing (BFHT) provides a straightforward method to evaluate multiple hypotheses at the same time and provides evidence that favors the null hypothesis or alternative hypothesis. In the present perspective, we show the merits of BFHT for three different use cases, including a clinical trial, basic research as well as a single case study. Here we show that Bayesian statistics is a viable addition of a scientist's statistical toolset, which can help to interpret data.
What Makes a Customer Brand Citizen in Restaurant Industry

Due to the crucial role of customers’ brand citizenship behaviors on brand strength, this study explored the relationship between brand uniqueness, brand credibility, brand intimacy, brand love, and brand citizenship behavior in Taiwan’s restaurant context. The participants are the customers of Wang Steak, a famous restaurant chain in Taiwan. A total of 358 valid responses were gathered from a questionnaire survey, with a response rate of 71.6%. We used structural equation modeling to analyze the data. Brand uniqueness, brand credibility, and brand intimacy all have a positive relationship with brand love. Moreover, brand love was positively associated with brand citizenship behavior. The findings provide greater insights into the relationship of perceived brand uniqueness, brand credibility, brand intimacy, and brand love with customers’ brand citizenship behaviors.
COMPARING THE PERFORMANCE OF OVERSAMPLING TECHNIQUES IN COMBINATION WITH A CLUSTERING ALGORITHM FOR IMBALANCED LEARNING

Imbalanced datasets in supervised learning are considered an ongoing challenging task for standard algorithms, seeing as they are designed to handle balanced class distributions and perform poorly when applied to problems of the imbalanced nature. Many methods have been developed to address this specific problem but the more general approach to achieve a balanced class distribution is data level modification, instead of algorithm modifications. Although class imbalances are responsible for significant losses of performance in standard classifiers in many different types of problems, another aspect that is important to consider is the small disjuncts problem. Therefore, it is important to consider and understand solutions that not only take into the account the between-class imbalance (the imbalance occurring between the two classes) but also the within-class imbalance (the imbalance occurring between the sub-clusters of each class) and to oversample the dataset by rectifying these two types of imbalances simultaneously. It has been shown that cluster-based oversampling is a robust solution that takes into consideration these two problems. This work sets out to study the effect and impact combining different existing oversampling methods with a clustering-based approach. Empirical results of extensive experiments show that the combinations of different oversampling techniques with the clustering algorithm k-means – K-Means Oversampling - improves upon classification results resulting solely from the oversampling techniques with no prior clustering step.
Using In-Mouth Sensors to Measure Head Kinematics in Rugby

Sports-related concussions were once thought to present only transient symptoms, but recent research has shown the potential for long-term neurological impairments. Previous research has instrumented helmeted athletes with sensors to measure head kinematics and concussive biomechanics on-field. However, expanding studies beyond helmeted players will help us to understand sex-specific differences and unhelmeted loading conditions and their relationship to impact exposure and tolerance. Athletes on the men’s and women’s club collegiate rugby teams at Virginia Tech were custom fit with instrumented mouthguards and monitored over a year to quantify sub-concussive and concussive biomechanics in unhelmeted athletes. The objective of this study was to quantify head kinematics sustained from inertially driven (body impacts) and direct head impacts in collegiate rugby players. The majority of the impacts collected were low in magnitude, and head impacts were associated with higher PLAs than body impacts (∆ = 2.5 g, p < 0.005). Head impacts had a median PLA of 15.2 g and a median ∆RV of 6.7 rad/s; body impacts had a median PLA of 13.6 g and a median ∆RV of 6.6 rad/s. For each impact type, men and women sustained similar peak linear accelerations (head impact: ∆ = 1.9 g, p = 0.219; body impact: ∆ = 0.5 g, p = 0.280). Men sustained higher changes in rotational velocities for head impacts (∆ = 1.4 rad/s, p = 0.003) and longer impact durations for both head (∆ = 2.6 ms, p < 0.001) and body (∆ = 2.8 ms, p = 0.023) impacts. These findings can be used to advise best practices for in-mouth sensors and better understand the mechanisms of concussion in unhelmeted sports.
Depth Mapping Accuracy Evaluation of the Microsoft Kinect v2 Motion Capture Sensor

animation with accurate human motion. These sensors and recording software create a cost-effective alternative to more traditional motion capture studio solutions. In addition to recording human motion, the depth mapping of any captured motion as well as the surrounding environment can be recorded and exported as a point cloud. This paper assesses the accuracy of three-dimensional depth mapping data recorded using multiple Microsoft Kinect v2 sensors at three different sites. Point clouds exported from the Kinect v2 sensors depth mapping were extracted from single points in time and compared to LiDAR collected by a laser scanner. An average of 82% of the depth sensor data was found to be ±1 inch (2.5 cm) of the laser scanning data with a standard deviation of 1.5%, and an average of 93% was found to be within ±1.5 inches (3.8 cm) of the laser scanning data with a standard deviation of 1.1%.
DECIPHERING THE MICROBIAL DIVERSITY OF TATTAPANI HO T WATER SPRING USING METAGENOMIC APPROACH

Heat stress due to changing environmental condition s is a problem as it negatively affects plant growth and development leading to reduction in crop yield. The rmal springs are major source of potentially signif icant but untapped microbial diversity encoding novel genes r elated to high temperature tolerance. The genes inv olved in the heat tolerance mechanism of thermophiles can serve as a valuable resource to improve high temperature tolerance in crop plants. Metagenomics bypasses the need for iso lation or cultivation of microorganisms as it deals with direct isolation of nucleic acids from environmental sampl es. The present study was designed to analyse the pr ofile of microbial community inhabiting hot water spring Tatt apani, Himachal Pradesh, India. The spring water tem perature at the discharge point was recorded as 65°C and pH 7.5. Two sets of universal 16S rRNA gene specific primers n amely U789F-U1053R and MGUF-MGUR were used to amplify partia l 16S rRNA gene (from V5-V6 and V3-V6 regions respectively) from metagenomic DNA. Sequencing and b ioinformatic analysis revealed that bacterial phylu m Proteobacteria was most abundant, constituting 36% a nd 40% of the total bacteria identified using V5-V6 and V3-V6 regions, respectively. Other phyla Thermodesulfobac teria (9.3%), Firmicutes (7.5%), Deinococcus-thermus (7%), Bacteriodates (2.5%), Aquificae (2%) were assigned to sequences amplified from V5-V6 region while phyla Fir micutes (18%), Bacteriodates (6%), Chloroflexi (2%) were assigned to sequences amplified from V3-V6 region. A significant number of sequences (30.5% and 20.5%) from region V3 -V6 and V5-V6 respectively did not show homology with existing database and were recorded as unclassified Bacteria. The information generated in this study wi ll help us in understanding the microbial ecology of hot springs.
Classification of Adverse Drug Reactions in Social Media

There are a large number of tweets with adverse drug reactions in social media, and the semantics of different levels of tweets are different. This article proposes a deep learning network method based on supervised learning, starting from different levels of tweets, Find the special semantics contained in different levels, then merge them, and use the BiLSTM method to build a classification model to find tweets related to adverse drug reactions. This article starts from the three levels of sentences; the first level: the character level, using the charCNN model to extract features at the character level; the second level of words, using the BERT model to extract features at this level Vector, third level: sentence level, use the syntactic dependency toolkit developed by Stanford to extract the relationship pairs between words in the sentence and vectorize them; after obtaining the three levels of feature vectors, use the BiLSTM model Build the classifier and finally complete the model.
Stability Analysis of Inverter-Based Systems with Virtual Inertia Control

This paper describes model development and stability analysis of a grid-forming inverter-based system with inertia emulation capability. A mathematical model is developed and linearized to obtain a small signal model of a two-inverter, single-load system; eigenvalue analysis is then conducted to analyse the stability aspects of emulating inertia using specialized power control loops. The system’s eigenvalues and their trajectories are compared with a small signal model of a grid-forming inverter with only droop controllers (no inertia emulation). Benchmark EMT models are also developed to compare the response of the small signal model to verify the accuracy of the developed linearized model and its predictions. Eigenvalue sensitivities of the generating units when operating with droop coefficients and inertial parameters are presented.
S1478951521002017jra 1..11

Objective. Virtual reality (VR) has the potential to improve pain and pain-related symptoms. We examined the feasibility, acceptability, safety, and impact of a 30-min virtual underwater/ sea environment (VR Blue) for reducing pain and pain-related symptoms in advanced colorectal cancer patients. A qualitative exit interview was conducted to understand preferences, thoughts, and feelings about the VR session. Method. Participants (N = 20) had stage IV colorectal cancer and moderate-to-severe pain. Participants completed a 30-min VR Blue session that visually and aurally immersed them in virtual ocean scenarios. Feasibility was assessed by accrual (N = 20), protocol adherence (≥80% completing VR Blue), and completed data (≥80% assessment completion). Acceptability was determined by patients reporting ≥80% intervention satisfaction. Safety was determined by ≥80% of patients completing the session without self-reported side effects. Measures of pain, tension, relaxation, stress, anxiety, and mood were collected before, during, and after the VR Blue session. A semi-structured qualitative interview was conducted after VR Blue to assess participants’ VR experiences. Results. All participants (100%) completed the VR Blue session. There was 100% data collection at the preand post-assessments. Satisfaction with VR Blue was high M = 3.3 (SD = 0.4) (83%). No significant side effects were reported. Pain decreased by 59% (Pre-M = 3 [1]; PostM = 1 [1]). Tension decreased by 74% (Pre-M = 30 [24]; Post-M = 8 [13]). Relaxation improved by 38% (Pre-M = 62 [21]); Post-M = 86 [17]). Stress decreased by 68% (Pre-M = 24 [24]; Post-M = 8 [14]). Anxiety decreased by 65% (Pre-M = 20 [23]; Post-M = 7 [13]). Mood improved by 70% (Pre-M = 13 [16]; Post-M = 4 [11]). Qualitative data suggested a positive response to the VR Blue protocol. Significance of results. This work supports the feasibility, acceptability, and safety of VR Blue for advanced colorectal cancer patients. Participants showed significant pre-post improvement in pain and pain-related symptoms hinting to the potential feasibility of VR interventions in this population. Larger, randomized trials with a control condition are needed to examine the efficacy of VR-based interventions for patients with advanced colorectal cancer and pain.
Learning Interpretable, High-Performing Policies for Autonomous Driving

Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD). AD requires interpretable and verifiable control policies that maintain high performance. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, RL approaches to produce high-performing, interpretable policies. The key to our approach is a procedure for allowing direct optimization in a sparse decision-tree-like representation. We validate ICCTs against baselines across six domains, showing that ICCTs are capable of learning interpretable policy representations that parity or outperform baselines by up to 33% in AD scenarios while achieving a 300x-600x reduction in the number of policy parameters against deep learning baselines. Furthermore, we demonstrate the interpretability and utility of our ICCTs through a 14-car physical robot demonstration.
BSIM3 parameters extraction of a 0.35 μm CMOS technology from 300K down to 77K

In this work, we present a commercial 0.35 μm/3.3 V CMOS technology behaviour study of both linear and gate-enclosed transistors from room temperature down to 77 Kelvin. Cryogenic setup used to complete this study is first described. Measurement results are then discused and a model based on a BSIM3 parameters extraction is proposed.
Transmission Capability of the Grid Cross Section Considering the Interruptible Load Management

This paper proposes a mathematical model of power grid section transmission limit under the constraints of N-1 contingency analysis and interruptible load management. By loosen the conditions of traditional N-1 analysis with the interruptible load management on demand side, an improved power grid section transmission limit model considering the interruptible load and power flow constraints is established. The opposite-quantities-adjusted in pairs method based on branch sensitivity is studied and proposed to solve the model. The quantitative relationship between the increments of the grid section transmission limit and the load interrupted quantities is tested with the IEEE39–bus testing system. The simulation result shows that the application of interruptible load can improve the grid section transmission limit, as well as improving the operation economy while the security of the power system is guaranteed.
Novel fiber optic distributed refractive index sensing method by optical frequency domain reflectometry

We proposed a novel fiber-optic distributed refractive index (RI) sensing method by optical frequency domain reflectometry (OFDR), which is qualified for most scenes such as solution diffusion process and multi-analytes detection that require distributed RI sensing ability. We use a section of multi-core fiber (MCF) as the sensing fiber, which consists of one central core and six outer surrounding cores. The MCF is etched by hydrofluoric acid until the outer cores are exposed to the air, thus the effective index of outer cores will be affected by the RI of the surrounding samples, which will lead the wavelength shift of the Rayleigh backscattering (RBS) spectrum. In order to realize distributed RI sensing, we introduce the OFDR technique, which has excellent ability of spatial localization. Then, by demodulating the wavelength shift of the local RBS spectrum, the distributed RI sensing is realized. The sensing length of 19 cm and the spatial resolution of 5.3 mm are achieved in experiment. The RI sensing range is as wide as 1.33~1.44 RIU, and the maximum sensitivity of 47 nm/RIU is obtained around 1.44 RIU. In addition, the central core can be used for temperature compensation because it keeps isolated to the surrounding sample. The experimental results indicate that the temperature sensitivity reaches 9.8 pm/°C during 25~80°C. By temperature compensation, the RI measurement error induced by temperature disturbance can be eliminated. Finally, we successfully detect the diffusion process of glycerol in water using the proposed fiber-optic distributed RI sensor.
Evaluation of Effectiveness of Majority Voting Method in Crowdsourcing-based Subtitling Methods

: Crowdsourcing is an effective way to generate subtitles for videos with multiple speakers. This study proposes and evaluates a word-by-word majority voting method as a new approach to creating high-accuracy subtitles from multiple types of subtitles created by multiple workers in crowdsourcing. In the word-by-word majority voting method, the accuracies of the subtitles themselves remarkably vary between high and low, since even a slight difference in word order, for example, can significantly reduce incorrect generation. This remarkable difference shows that the proposed method has potential to generate appropriate subtitles for videos having multiple speakers.
Cost-Effectiveness Analysis of Rapid Test Compared to Polymerase Chain Reaction (PCR) in Patients with Acute Respiratory Syndrome

Background: It is important to have a rapid and cost-effective laboratory test for the early diagnosis of respiratory diseases.The aim of this study was to analyze the cost-effectiveness of rapid tests and PCR in patients with suspected influenza. Methods: This study was a cost-effectiveness analysis from a community perspective that in which patients who were referred to the emergency department of selected hospitals of IUMS university with suspected respiratory symptoms of influenza were studied by convenience sampling method. The intervention and comparator were rapid tests and PCR respectively. effectiveness indicators in this study include sensitivity, specificity, positive and negative predictive value of both tests, and it examines costs from a community perspective. After drawing the decision tree model in the TreeAge software, the incremental cost-effectiveness ratio was calculated and to evaluate the strength of the analysis results, one-way and two-way sensitivity analyses on all cost and effectiveness parameters were used. Results: According to the findings of this study, the effectiveness index in rapid test and PCR is equal to 0.90 and 0.91, respectively, and the average cost of the two tests is equal to 62.157 and 201.37$, respectively, the ICERwas 25450.27 and the cost-effectiveness threshold was estimated equal to 6000 according to the per capita GDP of the country. One-way and two-way sensitivity analysis showed that the result of cost-effectiveness analysis did not change, and the rapid test is cost-effective. Conclusion: Rapid test is less costly and effective than PCR, but the cost difference is greater than the difference in effectiveness and in terms of effectiveness indicators, both diagnostic tests are almost similar, and this cost difference has led to the choice of the rapid test as a cost-effective option. Therefore, it is recommended that physicians prioritize rapid tests in the diagnosis of respiratory diseases.
MOBILE ROBOT LOCALIZATION USING WLAN, ODOMETRY AND GYROSCOPE DATA

In this paper a method for estimation of position and motion of a mobile robot in an indoor environment is introduced. The proposed method uses WLAN signal strength to estimate the global position of a mobile robot in an office building. Thus signal strengths of the received access points are stored in the radio map in calibration phase. In localization phase the stored values are compared with actually measured one's. Therefore a fingerprinting algorithm, that was introduced before, is used. The improvement of the presented work is the multi sensor fusion using Kalman filter, which enhances the accuracy of fingerprinting algorithms and tracking of the robot. For this reason odometric and gyroscopic sensors of the robot are fused with the estimated position of the fingerprinting algorithm. The paper presents the experimental results of measurements made in an office building.
Distance estimation of obstacles in monocular vision based on deep learning

Aiming at the forward-looking distance perception requirements of obstacles such as mountains, forests and trees during the ultra-low-altitude flight of subsonic aircraft, an obstacle distance estimation method based on deep learning is proposed. Firstly, the relationship between adjacent hyper-pixels is modeled by the conditional random fields, the neural network model is trained using the data set, and then the loss function of the pose parameter regression is designed, and the representation of the neural network model is input to the constructed loss function to optimize the weight of the regression model. Simulation experiments show that the proposed method can effectively perceive obstacles within 3km ahead, and the distance estimation error is not more than 20% of the relative distance, which can meet the obstacle avoidance requirements of most sudden obstacles during ultra-low-altitude flight.