Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesophyll specific expression of a bacterial mercury transporter-based vacuolar sequestration machinery sufficiently enhances mercury tolerance of Arabidopsis

We aimed to efficiently enhance plant Hg(II) tolerance by the transgenic approach utilizing a bacterial mercury transporter MerC, an Arabidopsis mesophyll specific promoter pRBCS1A, and a vacuolar membrane targeting syntaxin AtVAM3/SYP22. We generated two independent homozygous Arabidopsis pRBCS1A-TCV lines expressing mT-Sapphire-MerC-AtVAM3 under the control of pRBCS1A. Quantitative RT-PCR showed that the transgene was expressed specifically in shoots of pRBCS1A-TCV lines. Confocal analyses further demonstrated the leaf mesophyll specific expression of mT-Sapphire-MerC-AtVAM3. Confocal observation of the protoplast derived from the F1 plants of the pRBCS1A-TCV line and the tonoplast marker line p35S-GFP-δTIP showed the tonoplast colocalization of mT-Sapphire-MerC-AtVAM3 and GFP-δTIP. These results clearly demonstrated that mT-Sapphire-MerC-AtVAM3 expression in Arabidopsis is spatially regulated as designed at the transcript and the membrane trafficking levels. We then examined the Hg(II) tolerance of the pRBCS1A-TCV lines as well as the p35S-driven MerC-AtVAM3 expressing line p35S-CV under the various Hg(II) stress conditions. Short-term (12 d) Hg(II) treatment indicated the enhanced Hg(II) tolerance of both pRBCS1A-TCV and p35S-CV lines. The longer (3 weeks) Hg(II) treatment highlighted the better shoot growth of the transgenic plants compared to the wild-type Col-0 and the pRBCS1A-TCV lines were more tolerant to Hg(II) stress than the p35S-CV line. These results suggest that mesophyll-specific expression of MerC-AtVAM3 is sufficient or even better to enhance the Arabidopsis Hg(II) tolerance. The Hg accumulation in roots and shoots did not differ between the wild-type Col-0 and the MerC-AtVAM3 expressing plants, suggesting that the boosted Hg(II) tolerance of the transgenic lines would be attributed to vacuolar Hg-sequestration by the tonoplast-localized MerC. Further perspectives of the MerC-based plant engineering are also discussed.
Tank fouling community enhances coral microfragment growth

Anthropogenic stressors threaten reefs worldwide and natural in situ coral reproduction may be inadequate to meet this challenge. Land-based culture can provide increased coral growth, especially with microfragments. We tested whether culture methods using different algal fouling communities could improve the growth and health metrics of microfragments of the Hawaiian coral, Porites compressa. Culture method fouling communities were: 1) similar to a reef environment (Mini Reef); 2) clean tanks managed to promote crustose coralline algae (Clean Start); and, 3) tanks curated beforehand with poorly-competing algae (Green Film) assessed in winter and summer months. The Green Film method during the winter produced the fastest microfragment mean growth at 28 days until the first row of new polyps developed and the highest tank metric scores. Time efficient, standardized methods for land-based culture designed to maximize growth and production of coral fragments will contribute considerably to the success of large scale restoration efforts.
Optimization of Transcription Factor Genetic Circuits

Transcription factors (TFs) affect the expression of mRNAs. In essence, the TFs form a large computation network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day-night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance, and in how large TF networks may acquire a tendency for genetic variation and disease.
Sampling globally and locally correct RNA 3D structures using ERNWIN, SPQR and experimental SAXS data

The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray spectroscopy can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (ERNWIN) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit solvent. We apply the procedure on five different structures up to 126 nucleotides with promising results.
Endogenous μ-opioid – neuropeptide Y Y1 receptor synergy silences chronic postoperative pain

Tissue injury creates a delicate balance between latent pain sensitization (LS) and compensatory endogenous analgesia. Inhibitory G protein-coupled receptor (GPCR) interactions that oppose LS, including μ-opioid receptor (MOR) and neuropeptide Y Y1 receptor (Y1R) activity, persist in the spinal cord dorsal horn (DH) for months, even after the resolution of normal pain thresholds. Here, we demonstrate that following recovery from surgical incision, a potent endogenous analgesic synergy between MOR and Y1R activity persists within DH interneurons to reduce the intensity and duration of latent postoperative hyperalgesia and ongoing pain. Failure of such endogenous GPCR signaling to maintain LS in remission may underlie the transition from acute to chronic pain states.
RFCam

As cameras and Wi-Fi access points are widely deployed in public places, new mobile applications and services can be developed by connecting live video analytics to the mobile Wi-Fi-enabled devices of the relevant users. To achieve this, a critical challenge is to identify the person who carries a device in the video with the mobile device's network ID, e.g., MAC address. To address this issue, we propose RFCam, a system for human identification with a fusion of Wi-Fi and camera data. RFCam uses a multi-antenna Wi-Fi radio to collect CSI of Wi-Fi packets sent by mobile devices, and a camera to monitor users in the area. With low sampling rate CSI data, RFCam derives heterogeneous embedding features on location, motion, and user activity for each device over time, and fuses them with visual user features generated from video analytics to find the best matches. To mitigate the impacts of multi-user environments on wireless sensing, we develop video-assisted learning models for different features and quantify their uncertainties, and incorporate them with video analytics to rank moments and features for robust and efficient fusion. RFCam is implemented and tested in indoor environments for over 800 minutes with 25 volunteers, and extensive evaluation results demonstrate that RFCam achieves real-time identification average accuracy of 97.01% in all experiments with up to ten users, significantly outperforming existing solutions.
EarlyScreen

Emotion dysregulation in early childhood is known to be associated with a higher risk of several psychopathological conditions, such as ADHD and mood and anxiety disorders. In developmental neuroscience research, emotion dysregulation is characterized by low neural activation in the prefrontal cortex during frustration. In this work, we report on an exploratory study with 94 participants aged 3.5 to 5 years, investigating whether behavioral measures automatically extracted from facial videos can predict frustration-related neural activation and differentiate between low- and high-risk individuals. We propose a novel multi-scale instance fusion framework to develop EarlyScreen - a set of classifiers trained on behavioral markers during emotion regulation. Our model successfully predicts activation levels in the prefrontal cortex with an area under the receiver operating characteristic (ROC) curve of 0.85, which is on par with widely-used clinical assessment tools. Further, we classify clinical and non-clinical subjects based on their psychopathological risk with an area under the ROC curve of 0.80. Our model's predictions are consistent with standardized psychometric assessment scales, supporting its applicability as a screening procedure for emotion regulation-related psychopathological disorders. To the best of our knowledge, EarlyScreen is the first work to use automatically extracted behavioral features to characterize both neural activity and the diagnostic status of emotion regulation-related disorders in young children. We present insights from mental health professionals supporting the utility of EarlyScreen and discuss considerations for its subsequent deployment.
SafeGait

Recent works have shown that wearable or implanted devices attached at different locations of the body can generate an identical security key from their independent measurements of the same gait. This has created an opportunity to realize highly secured data exchange to and from critical implanted devices. In this paper, we first demonstrate that vision can be used to easily attack such gait-based key generations; an attacker with a commodity camera can measure the gait from a distance and generate a security key with any target wearable or implanted device faster than other legitimate devices worn at different locations of the subject's body. To counter the attack, we propose a firewall to stop video-based gait measurements to proceed with key generation, but letting measurements from inertial measurement units (IMUs) that are widely used in wearable devices to measure the gait accelerations from the body to proceed. We implement the firewall concept with an IMU-vs-Video binary classifier that combines InceptionTime, an ensemble of deep Convolutional Neural Network (CNN) models for effective feature extraction from gait measurements, to a Generative Adversarial Network (GAN) that can generalize the classifier across subjects. Comprehensive evaluation with a real-world dataset shows that our proposed classifier can perform with an accuracy of 97.82%. Given that an attacker has to fool the classifier for multiple consecutive gait cycles to generate the complete key, the high single-cycle classification accuracy results in an extremely low probability for a video attacker to successfully pair with a target wearable device. More precisely, a video attacker would have one in a billion chance to successfully generate a 128-bit key, which would require the attacker to observe the subject for thousands of years.
DiverSense

The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.
MIMO array for short‐range, high‐resolution automotive sensing

The study introduces the concept of multiple input–multiple output (MIMO) radar or sonar arrays for short-range, high-resolution sensing in vehicular applications. The use of a MIMO architecture, which is becoming increasingly popular in this field, is selected to reduce the amount of physical elements in the array needed for beamforming, but also to allow signal processing approaches for forming narrow beams in the near-field of the array. The study analytically derives the proposed signal processing approach, and then verifies it via simulated and experimental data in a laboratory environment with scientific equipment assembled for this purpose.
Predicting vacant parking space availability: an SVR method with fruit fly optimisation

In this study, a novel prediction model for the number of vacant parking spaces after a specific period of time is proposed based on support vector regression (SVR) with fruit fly optimisation algorithm (FOA). In the proposed model, the SVR parameters are initialised as the fruit fly population, and FOA is utilised to search the optimal parameters for SVR. Sufficient experiments within various scenarios, i.e. predicting the vacant parking space availability in parking lots with various capacities after various periods of time, have been conducted to verify the effectiveness of the proposed FOA-SVR prediction model. Three other commonly used prediction models, i.e. backpropagation neural network (NN), extreme learning machine and wavelet NN, are used as the comparison models. The experimental results show that the proposed FOA-SVR method has higher accuracy and stability in all the prediction scenarios.
Hotspot identification considering daily variability of traffic flow and crash record: A case study

Abstract Hotspots identification (HSID), a reactive crash prediction based on the historical accident counts, is crucial to transport authorities for evaluating the risk level of the object road sites. The objective of the research is to identify unidentified hotspots that should have been treated. Numerous conventional HSID approaches have been developed and applied for decades, none of which takes daily variability of traffic flow and crash record into account. In this regard, we categorize the time of day into four groups: (1) morning peak hours, (2) afternoon peak hours, (3) daytime, and (4) night off-peak hours. The authors further apply this proposed methodology to Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast based on an Empirical Bayesian (EB) approach. Finally, the applications of these proposed EB-based methods and the conventional EB method are discussed through an aggregated view.
Prototype of smart office system using based security system

Creating a new technology in the modern era gives a positive impact on business and industry. Internet of Things (IoT) as a new communication technology is very useful in realizing smart systems such as: smart home, smart office, smart parking and smart city. This study presents a prototype of the smart office system which was designed as a security system based on IoT. Smart office system development method used waterfall model. IoT-based smart office system used platform (project builder) cayenne so that. The data can be accessed and controlled through internet network from long distance. Smart office system used arduino mega 2560 microcontroller as a controller component. In this study, Smart office system is able to detect threats of dangerous objects made from metals, earthquakes, fires, intruders or theft and perform security monitoring outside the building by using raspberry pi cameras on autonomous robots in real time to the security guard.
A Bibliographic Survey of Sentiment Classification using Hybrid Ensemble-based Machine Learning Approaches

The rapid number of reviews on different fields have contributed to the rising field of data analysis. Several methods are existing for data analysis but there is a need to find the right methodology that can provide better accuracy. The objective of the paper is to find an accurate method depending upon the type of dataset. Previous researches have primarily relied on using the KNN approach and issues for deciding the K-value. For the research work, the data from the Statistics Department of the University of Wisconsin-Madison has been taken to evaluate the teacher performance. The hybrid approach uses three different machine learning models for prediction. The prediction model was tested effectively using the teacher assistant evaluation dataset. The hybrid approach has been developed to improve the identification of teacher performance. Our findings indicate that on combining KNN, decision tree, and naïve Bayes, there is a considerable increase in the performance of the prediction analysis. The results have shown that the hybrid approach called KDN (KNN, Decision Tree, Naïve Bayes) obtained better results with 53.04% accuracy as compared to the baseline system performance.
Distribution Pattern of N6-Methyladenine DNA Modification in the Seashore Paspalum (Paspalum vaginatum) Genome

N6-methyladenine (6mA) DNA modification has been detected in several eukaryotic organisms, in some of them, it plays important role in the regulation process of stress-resistance response. However, the genome-wide distribution patterns and potential functions of 6mA DNA modification in halophyte Seashore paspalum (Paspalum vaginatum) remain largely unknown. Here, we examined the 6mA landscape in the P. vaginatum genome by adopting single molecule real-time sequencing technology and found that 6mA modification sites were broadly distributed across the P. vaginatum genome. We demonstrated distinct 6mA methylation levels and 6mA distribution patterns in different types of transcription genes, which hinted at different epigenetic rules. Furthermore, the moderate 6mA density genes in P. vaginatum functionally correlated with stress resistance, which also maintained a higher transcriptional level. On the other hand, a specific 6mA distribution pattern in the gene body and near TSS was observed in gene groups with higher RNA expression, which maybe implied some kind of regularity between 6mA site distribution and the protein coding genes transcription was possible. Our study provides new insights into the association between 6mA methylation and gene expression, which may also contribute to key agronomic traits in P. vaginatum.
Co-designing an Indicator of Habitat Connectivity for England

Landscapes have been drastically transformed by human activities, generally resulting in the loss of semi-natural habitat. In the United Kingdom, wildlife habitat mainly consists of small patches of semi-natural habitat that are poorly connected to each other. In May 2019 the United Kingdom Government published an outcome indicator framework for measuring progress against the goals and outcomes of the 25 Year Environment Plan (YEP) for England. The indicator of the Quantity, Quality and Connectivity of Habitats (D1) is one of seven indicators within the Wildlife theme and it follows the principle of making areas of semi-natural habitat “more, bigger, better and joined up. ” In this study, we describe the process of co-designing the connectivity metric for indicator D1. In consultation with experts and stakeholders we selected three candidate landscape connectivity metrics to produce the indicator. The first metric comes from a suite of rules of thumb for practitioners and it is the proportion of habitat patches in the landscape that have a nearest neighbor ≤ 1 km away. The second metric is a habitat fragmentation index from the Natural England National Biodiversity Climate Change Vulnerability Assessment Tool (NBCCVAT). The third and final metric is from the software Condatis and it represents the ability of a species to move through a landscape. We tested each metric on a set of simulated landscapes representing different levels of habitat addition strategies and different spatial configurations. We asked if the metrics are able to detect changes in the connectivity of each of these landscapes after habitat addition. Two of the three metrics (NBCCVAT and Condatis) performed well and were sensitive to change. They both increased as the total extent of habitat increased and each showed particular sensitivity to one spatial arrangement over the other. Given these results, one or both of these metrics could be used to produce the indicator. We discuss the implications of using one or both of the metrics and highlight the fundamental choices that need to be made to produce the indicator.
Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.