Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual coding potential of a 2′,5′-branched ribonucleotide in DNA

Branchpoints in RNA templates are highly mutagenic, but it is not known yet whether this also applies to branchpoints in DNA templates. Here, we report how nucleic acid polymerases replicate a 2′,5′-branched DNA (bDNA) molecule. We constructed long-chained bDNA templates containing a branch guanosine and T7 promoters at both arms by splinted ligation. Quantitative real-time PCR analysis was used to investigate whether a branchpoint blocks DNA synthesis from the two arms in the same manner. We find that the blocking effect of a branchpoint is arm-specific. DNA synthesis from the 2′-arm is more than 20,000-fold decreased, whereas from the 3′-arm only 15-fold. Our sequence analysis of full-length nucleic acid generated by Taq DNA polymerase, Moloney murine leukemia virus reverse transcriptase, and T7 RNA polymerase from the 2′-arm of bDNA shows that the branched guanine has a dual coding potential and can base-pair with cytosine and guanine. We find that branchpoint templating is influenced by the type of the surrounding nucleic acid and is probably modulated by polymerase and RNase H active sites. We show that the branchpoint bypass by the polymerases from the 3′-arm of bDNA is predominantly error-free, indicating that bDNA is not as highly mutagenic as 2′,5′-branched RNA.
On the Continuous and Reactive Analysis of a Variety of Spatio-Temporal Data

Reactive decision making on heterogeneous streaming data is gaining importance in a wide range of situations, e.g., in the electricity management domain for reacting to anomaly consumption or in oil and gas extraction sites to detect dangerous situations. Modern cities represent a relevant scenario for reactive decision making because of the vast number of stakeholders willing to benefit from the growing availability of streams of data from various sources. In the state-of-the-art, this problem is addressed through ad-hoc solutions that fit only a specific scenario. In this paper, I report on the models and technical implementations, which I propose to enable reactive analysis of a variety of spatio-temporal data, and on their evaluation in real-world scenarios to prove their adequacy.
Physiological and Molecular Responses to Altered Sodium Intake in Rat Pregnancy

Background In pregnancy, a high plasma volume maintains uteroplacental perfusion and prevents placental ischemia, a condition linked to elevated maternal blood pressure (BP). Reducing BP by increasing Na+ intake via plasma volume expansion appears contra‐intuitive. We hypothesize that an appropriate Na+ intake in pregnancy reduces maternal BP and adapts the renin‐angiotensin system in a pregnancy‐specific manner. Methods and Results BP was measured by implanted telemetry in Sprague‐Dawley rats before and throughout pregnancy. Pregnant and nonpregnant animals received either a normal‐salt (0.4%; NS), high‐salt (8%; HS), or low‐salt (0.01%; LS) diet, or HS (days 1–14) followed by LS (days 14–20) diet (HS/LS). Before delivery (day 20), animals were euthanized and organs collected. Food, water, and Na+ intake were monitored in metabolic cages, and urinary creatinine and Na+ were analyzed. Na+ intake and retention increased in pregnancy (NS, LS), leading to a positive Na+ balance (NS, LS). BP was stable during LS, but reduced in HS conditions in pregnancy. The renin‐angiotensin system was adapted as expected. Activating cleavage of α‐ and γ‐subunits of the renal epithelial Na+ channel and expression of‐full length medullary β‐subunits, accentuated further in all LS conditions, were upregulated in pregnancy. Conclusions Pregnancy led to Na+ retention adapted to dietary changes. HS exposure paradoxically reduced BP. Na+ uptake while only modestly linked to the renin‐angiotensin system is enhanced in the presence of posttranslational renal epithelial Na+ channel modifications. This suggests (1) storage of Na+ in pregnancy upon HS exposure, bridging periods of LS availability; and (2) that potentially non–renin‐angiotensin–related mechanisms participate in ENaC activation and consecutive Na+ retention.
Target Detection Based on Cascade Network and Densely Connected Network in Remote Sensing Image

In order to solve the problem of low detection efficiency of remote sensing image using convolutional neural network. a faster detection algorithm based on the three levels cascade convolutional densely connected network model is established to target detection in high resolution remote sensing images. Firstly, the shallow full convolutional network is used as the first layer of cascaded network. The shallow layer network runs fast, and it can quickly filter the incorrect window in the remote sensing image. The full convolutional network can build the sliding window into the network and improve the efficiency. Secondly, the shallow network is used as the second layer network to filter the windows again after the first layer network detected. Thirdly, the third layer network uses a small densely connected network (DenseNet). Densely connected network have fewer parameters and are more accurate. It can identify the reserved windows of second layer network. The data used in the test are No.2 High resolution satellite remote sensing images. The experiment shows that for the same size picture, in the case of little difference in the correct rate, the computation time of convolutional neural network is about 1.3-1.8 times of the computing time of cascaded network.
Humanoid Robot Motion Modeling Based on Time-Series Data Using Kernel PCA and Gaussian Process Dynamical Models

In this article, contrary to popular studies on human motion learning, we focus on addressing the problem of humanoid robot motions directly. Performances of different kernel functions with principal components analysis (PCA) in Gaussian process dynamical models (GPDM) are investigated to build efficient humanoid robot motion models. A novel kernel-PCA-GPDM method is proposed for building different types of humanoid robot motion models. Compared with the standard-PCA-GPDM and auto-encoder-GPDM methods, our proposed method is more efficient in humanoid robot motion modeling. In this work, three types of NAO robot motion models are studied: walk-model, lateral-walk model, and wave-hand model, where motion data are collected from an Aldebaran NAO robot using magnetic rotary encoder sensors. Using kernel-PCA-GPDM method, the motion data are first projected from the high 23-dimension observation space to a 3-dimension low latent space. Then, three types of humanoid robot motion models are learned in the 3D latent space. Compared with other kernel-PCA-GPDM or auto-encoder-GPDM methods, our proposed novel kernel-PCA-GPDM method performs efficiently in motion learning. Finally, we realize humanoid robot motion representation to verify the motion models that we build. The experimental results show that our proposed kernel-PCA-GPDM method builds efficient and smooth motion models.
ReviewQA: a relational aspect-based opinion reading dataset

Deep reading models for question-answering have demonstrated promising performance over the last couple of years. However current systems tend to learn how to cleverly extract a span of the source document, based on its similarity with the question, instead of seeking for the appropriate answer. Indeed, a reading machine should be able to detect relevant passages in a document regarding a question, but more importantly, it should be able to reason over the important pieces of the document in order to produce an answer when it is required. To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that we expect a model to master. Indeed, each question comes with an associated type that characterizes the required competency. With this framework, it is possible to benchmark the main families of models and to get an overview of what are the strengths and the weaknesses of a given model on the set of tasks evaluated in this dataset. Our corpus contains more than 500.000 questions in natural language over 100.000 hotel reviews. Our setup is projective, the answer of a question does not need to be extracted from a document, like in most of the recent datasets, but selected among a set of candidates that contains all the possible answers to the questions of the dataset. Finally, we present several baselines over this dataset.
Noise Sensitivity of Local Descriptors vs ConvNets: An application to Facial Recognition

The Local Binary Patterns (LBP) is a local descriptor proposed by Ojala et al to discriminate texture due to its discriminative power. However, the LBP is sensitive to noise and illumination changes. Consequently, several extensions to the LBP such as Median Binary Pattern (MBP) and methods such as Local Directional Pattern (LDP) have been proposed to address its drawbacks. Though studies by Zhou et al, suggest that the LDP exhibits poor performance in presence of random noise. Recently, convolution neural networks (ConvNets) were introduced which are increasingly becoming popular for feature extraction due to their discriminative power. This study aimed at evaluating the sensitivity of ResNet50, a ConvNet pre-trained model and local descriptors (LBP and LDP) to noise using the Extended Yale B face dataset with 5 different levels of noise added to the dataset. In our findings, it was observed that despite adding different levels of noise to the dataset, ResNet50 proved to be more robust than the local descriptors (LBP and LDP).
Simulation Study of a GPC Controller for a Hydroelectric Plant during Electric Faults

This paper presents the simulation study where the classic speed controller of a hydraulic power unit has been replaced with an unrestricted generalized predictive controller. The model of the power plant includes an excitation system, subsystem (speed controller, servo motor and turbine), load 1, load 2, Three-phase Transformer, External Grid and Fault. The electric three-phase fault has 0.1 seconds of duration. The results are compared with the responses of a conventional proportional integrative and derivative controller. The library of simulation developed by MathWorks “Power-Turbine”(Simulink) and for Matlab is used, also this research includes the steps for modify the library and so adapt it for our purposes.
B-Mode Ultrasound Based Diagnosis of Liver Cancer With CEUS Images as Privileged Information

Contrast-enhanced ultrasound (CEUS) is a valuable imaging modality for diagnosis of liver cancers. However, the complexity of CEUS-based diagnosis limits its wide application, and the B-mode ultrasound (BUS) is still the most popular diagnosis modality in clinical practice. In order to promote BUS-based computer-aided diagnosis (CAD) for liver cancers, we propose a learning using privileged information (LUPI) based CAD with BUS as the diagnosis modality and CEUS as PI. Particularly, the multimodal restricted Boltzmann machine (MRBM) works as a LUPI paradigm. That is, one BUS image and three CEUS images from the arterial phase, portal venous phase and delayed phase, respectively, are used to train three multimodal restricted Boltzmann machine (MRBM) models during training stage, but only the BUS data will be fed to MRBM to generate new feature representation at testing phase. A multiple empirical kernel learning machine (MEKLM) classifier is then performed on three new feature vectors from three MRBM models for classification of liver cancers. The experimental results show that the proposed MRBM-MEKLM algorithm outperforms all the compared algorithms, suggesting the effectiveness of the proposed LUPI-based CAD for liver cancer.
Insight Into the Compatibility Between C6F12O and Metal Materials: Experiment and Theory

C<sub>6</sub>F<sub>12</sub>O has been introduced as a potential substitute to the most greenhouse gas SF<sub>6</sub> used in the medium-voltage (MV) electrical equipment due to its great insulation and environmental performance. While there are few studies on the compatibility between C<sub>6</sub>F<sub>12</sub>O and metal materials used in the equipment which needs to be investigated and understood to avoid the negative impact on the material or gas. In this paper, we carried out experimental and theoretical studies on the interaction between C<sub>6</sub>F<sub>12</sub>O and copper, silver, aluminum. We found that the interaction between C<sub>6</sub>F<sub>12</sub>O and copper, aluminum introduces fluorine to the metal surface and generates some metal oxide. There was no significant change in the element composition and surface chemical states of silver after interacting with C<sub>6</sub>F<sub>12</sub>O. The interaction mechanism between C<sub>6</sub>F<sub>12</sub>O and copper, aluminum is attributed to the chemical adsorption and the interaction between C<sub>6</sub>F<sub>12</sub>O and silver belongs to the physical adsorption. The compatibility between C<sub>6</sub>F<sub>12</sub>O and silver is greater than that of copper, aluminum. Relevant results provide a reference for the design and manufacture of equipment using C<sub>6</sub>F<sub>12</sub>O gas mixture as the insulating medium.
Semantic Model Extensibility in Interoperable IoT Data Marketplaces: Methods, Tools, Automation Aspects

The proposed research is concerned with the problem of extending semantic models used in the IoT data-sharing contexts with new elements. These suggestions are made by the domain experts at run time and then validated by the ontology engineers. The process of terms suggestion is organized as a modeling dialogue: dynamically generated user interface forms with labels in natural language assist the domain experts in the creation of structured proposals. We hypothesize that this communication procedure will increase the efficiency and the perceived usability of performing change operations at different stages of the ontology evolution process. We then suggest a series of experiments to evaluate our approach.
Thalamocortical and Intracortical Inputs Differentiate Layer-Specific Mouse Auditory Corticocollicular Neurons

Long-range descending projections from the auditory cortex play key roles in shaping response properties in the inferior colliculus. The auditory corticocollicular projection is massive and heterogeneous, with axons emanating from cortical layers 5 and 6, and plays a key role in directing plastic changes in the inferior colliculus. However, little is known about the cortical and thalamic networks within which corticocollicular neurons are embedded. Here, laser scanning photostimulation glutamate uncaging and photoactivation of channelrhodopsin-2 were used to probe the local and long-range network differences between preidentified layer 5 and layer 6 auditory corticocollicular neurons from male and female mice in vitro. Layer 5 corticocollicular neurons were found to vertically integrate supragranular excitatory and inhibitory input to a substantially greater degree than their layer 6 counterparts. In addition, all layer 5 corticocollicular neurons received direct and large thalamic inputs from channelrhodopsin-2-labeled thalamocortical fibers, whereas such inputs were less common in layer 6 corticocollicular neurons. Finally, a new low-calcium/synaptic blockade approach to separate direct from indirect inputs using laser photostimulation was validated. These data demonstrate that layer 5 and 6 corticocollicular neurons receive distinct sets of cortical and thalamic inputs, supporting the hypothesis that they have divergent roles in modulating the inferior colliculus. Furthermore, the direct connection between the auditory thalamus and layer 5 corticocollicular neurons reveals a novel and rapid link connecting ascending and descending pathways. SIGNIFICANCE STATEMENT Descending projections from the cortex play a critical role in shaping the response properties of sensory neurons. The projection from the auditory cortex to the inferior colliculus is a massive, yet poorly understood, pathway emanating from two distinct cortical layers. Here we show, using a range of optical techniques, that mouse auditory corticocollicular neurons from different layers are embedded into different cortical and thalamic networks. Specifically, we observed that layer 5 corticocollicular neurons integrate information across cortical lamina and receive direct thalamic input. The latter connection provides a hyperdirect link between acoustic sensation and descending control, thus demonstrating a novel mechanism for rapid “online” modulation of sensory perception.
Effectiveness of the RAPAEL Smart Board for Upper Limb Therapy in Stroke Survivors: A Pilot Controlled Trial

We aim to assess the effectiveness of using the RAPAEL Smart Board as an assistive tool for therapists in clinical rehabilitation therapy settings and to investigate if it can be used to improve the motor recovery rate of stroke survivors. The RAPAEL Smart Board is a therapy tool where therapists actively engage patients, giving necessary verbal and physical interventions as in traditional treatment sessions. We conducted a randomized controlled study with 17 stroke survivors. An experimental group received therapy using the RAPAEL Smart Board for 30 minutes a day, 5 days per week, for 4 weeks in addition to their traditional treatments (i.e., 30 minutes of functional arm movement therapy). A control group received two 30-minute sessions of traditional treatment 5 days per week, for 4 weeks. The upper-extremity function was measured using the Wolf Motor Function Test before and after the 4-week interventions. Our results demonstrate that using the RAPAEL Smart Board, in combination with traditional treatment, significantly improves motor recovery when compared to traditional treatments alone.
Genetic Diversity, Population Structure, and Linkage Disequilibrium in a Spanish Common Bean Diversity Panel Revealed through Genotyping-by-Sequencing

A common bean (Phaseolus vulgaris) diversity panel of 308 lines was established from local Spanish germplasm, as well as old and elite cultivars mainly used for snap consumption. Most of the landraces included derived from the Spanish common bean core collection, so this panel can be considered to be representative of the Spanish diversity for this species. The panel was characterized by 3099 single-nucleotide polymorphism markers obtained through genotyping-by-sequencing, which revealed a wide genetic diversity and a low level of redundant material within the panel. Structure, cluster, and principal component analyses revealed the presence of two main subpopulations corresponding to the two main gene pools identified in common bean, the Andean and Mesoamerican pools, although most lines (70%) were associated with the Andean gene pool. Lines showing recombination between the two gene pools were also observed, most of them showing useful for snap bean consumption, which suggests that both gene pools were probably used in the breeding of snap bean cultivars. The usefulness of this panel for genome-wide association studies was tested by conducting association mapping for determinacy. Significant marker–trait associations were found on chromosome Pv01, involving the gene Phvul.001G189200, which was identified as a candidate gene for determinacy in the common bean.
Disturbance Attenuation in Distance-Based Formation Control: A Linear Matrix Inequality Approach

In this paper, we discuss disturbance attenuation problem in distance-based formation control under the exogenous disturbance. Based on the Lyapunov stability theory, we derive the matrix inequality condition for local asymptotic boundedness of the overall squared-distance errors of multiagent system. Through simulations, we could see that there exists a positive definite symmetric matrix of linear matrix inequality(LMI), with bounded squared-distance errors of formation system.
Novel Near-Optimal Scalar Quantizers with Exponential Decay Rate and Global Convergence

Many modern distributed real-time signal sensing/monitoring systems require quantization for efficient signal representation. These distributed sensors often have inherent computational and energy limitations. Motivated by this concern, we propose a novel quantization scheme called approximate Lloyd-Max that is nearly-optimal. Assuming a continuous and finite support probability distribution of the source, we show that our quantizer converges to the classical Lloyd-Max quantizer with increasing bitrate. We also show that our approximate Lloyd-Max quantizer converges exponentially fast with the number of iterations. The proposed quantizer is modified to account for a relatively new quantization model which has envelope constraints, termed as the envelope quantizer. With suitable modifications we show optimality and convergence properties for the equivalent approximate envelope quantizer. We illustrate our results using extensive simulations for different finite support source distributions.
Variation in the Correlation Between Heart Rate and Session Rating of Perceived Exertion-Based Estimations of Internal Training Load in Youth Soccer Players.

PURPOSE When exposed to the same external load, players receive different internal loads, resulting in varied adaptations in fitness. In adult soccer, internal training load is measured using heart rate (HR) and session rating of perceived exertion (sRPE) scales, but these have been underutilized in youth soccer. This study investigated the in-season variation in correlation between HR and sRPE estimations of training load for adolescent soccer players. METHOD Fifteen male professional adolescent players were monitored for 7 months. Within-participant correlations and Bland-Altman agreement plots for HR and sRPE were calculated for each month to analyze variation over the season and for individual players to analyze the validity of the scale. RESULTS The monthly correlations ranged from r = . 60 to r = . 73 (P < .05) and the overall correlation was r = . 64 (95% confidence interval, .60-.68; P < .001). Bland-Altman plots showed an agreement of methods. CONCLUSION Results showed consistently large correlations for all months. sRPE is a consistent method of measure of internal training load for the entire season for youth soccer players. Validity analysis found no bias in sRPE measurements when compared with HR for all players in the study.
Improving Prediction Accuracy in LSTM Network Model for Aircraft Testing Flight Data

Testing flight of new aircrafts is a critical step of the aircraft manufacturing process. The test flight will generate a huge amount of sensor data. Engineers can extract important information from these data and perform anomaly detection of the aircraft with the help of the information. The LSTM model currently in use during the test flight has many disadvantages. The main disadvantage is that the prediction result is not accurate enough, and the LSTM model's predicted value image does not fit the actual value image well. In addition, the input data dimension of the model is too high, resulting in a large amount of computation. However, the commonly used data dimension reduction methods will affect the accuracy of the results. Based on the above issues, the purpose of this paper is to optimize the LSTM network model for handling test flight data, and greatly improve the accuracy of the LSTM model prediction results. The optimization starts from the three perspectives of LSTM model parameters, LSTM model structure, and feature extraction in data preprocessing to improve the performance of the model.