Miracle-dz/UniFAR-FTUs huggingface dataset:
Rootstock–Scion Interaction Affects Cadmium Accumulation and Tolerance of Malus

To understand the roles of Malus rootstock, scion, and their interaction in Cd accumulation and tolerance, four scion/rootstock combinations consisting of the apple cultivars “Hanfu” (HF) and “Fuji” (FJ) grafted onto M. baccata (Mb) or M. micromalus “qingzhoulinqin” (Mm) rootstocks differing in relative Cd tolerance were exposed either to 0 µM or 50 µM CdCl2 for 18 d. Cd accumulation and tolerance in grafted Malus plants varied within rootstock, scion, and rootstock–scion interaction. Cd-induced decreases in photosynthesis, photosynthetic pigment level, and biomass were lower for HF grafted onto Mb than those for HF grafted onto Mm. Reductions in growth and photosynthetic rate were always the lowest for HF/Mb. Cd concentration, bioconcentration factor (BCF), and translocation factor (Tf ) were always comparatively higher in HF and FJ grafted onto rootstock Mm than in HF and FJ grafted on Mb, respectively. When HF and FJ were grafted onto the same rootstock, the root Cd concentrations were always higher in HF than FJ, whereas the shoot Cd concentrations displayed the opposite trend. The shoot Cd concentrations and Tf were lower for HF/Mb than the other scion/rootstock combinations. Rootstock, scion, and rootstock–scion interaction also affected subcellular Cd distribution. Immobilization of Cd in the root cell walls may be a primary Cd mobility and toxicity reduction strategy in Malus. The rootstock and scion also had statistically significant influences on ROS level and antioxidant activity. Cd induced more severe oxidative stress in HF and FJ grafted onto Mm than it did in HF and FJ grafted onto Mb. Compared with FJ, HF had lower foliar O2 -, root H2O2, and root and leaf MDA levels, but higher ROS-scavenging capacity. The rootstock, scion, and rootstock–scion interaction affected the mRNA transcript levels of several genes involved in Cd uptake, transport, and detoxification including HA7, FRO2-like, NRAMP1, NRAMP3, HMA4, MT2, NAS1, and ABCC1. Hence, the responses of grafted Malus plants to Cd toxicity vary with rootstock, scion, and rootstock–scion interaction.
Sex Manipulation Technologies Progress in Livestock: A Review

Sex manipulation technologies allow predetermination of the sex of animal offspring by altering the normal reproductive process. In livestock production, the difference in type and gender can translate into significant economic benefits, including alleviation of severe food shortages. In livestock, however, the commercial application of sex manipulation technologies is currently available for cattle only. In this review, we described the brief history of sex manipulation, and the research progresses of common methods used in sex manipulation thus far. Information presented in this review can inform future studies on expanding the scope and use of sex manipulation technologies in livestock.
Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Background: Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative. Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls. Methods: Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups. Results: Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (r = 0.43, p < 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = −0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (p < 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all p < 0.001). Conclusions: Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.
Use of Size Stratification and Differential Weighting to Measure Forest Trends

The gradient description of forest composition provides a basis upon which definitions of vegetational change can be formulated. Two techniques are described for the measurement of both general and gradient compositional change. One consists of segmenting the stand data into diameter classes. The difference between relative density values of species in adjacent size classes gives a measure of the rate of general compositional change within the stand. A line graph of compositional index by size class provides a graphical summary of the rate of compositional change of a stand along a particular gradient. Rates of compositional change within second-growth stands are shown to be more rapid on fine-textured soils than on sandy soils. "Compositional profiles" of two old-growth stands, one dominated by Tsuga canadensis and the other by Acer saccharum and Tilia americana, demonstrate that stable composition can be reached at various positions along the compositional gradient. Secondly, by weighting the species adaptation values by density and size, an integration of either general or gradient compositional change may be accomplished. Both general and gradient compositional change in northern Wisconsin upland forests form positively skewed frequency distributions. Although gradient change is positive in the majority of stands, a significant number are actually shifting toward lower positions along the compositional gradient.
Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network

Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with unsupervised CD methods and a CD network subjected to postprocessing by effectively utilizing the deep learning technique and object information.
Tyro3 Contributes to Retinal Ganglion Cell Function, Survival and Dendritic Density in the Mouse Retina

Retinal ganglion cells (RGCs) are the only output neurons of the vertebrate retina, integrating signals from other retinal neurons and transmitting information to the visual centers of the brain. The death of RGCs is a common outcome in many optic neuropathies, such as glaucoma, demyelinating optic neuritis and ischemic optic neuropathy, resulting in visual defects and blindness. There are currently no therapies in clinical use which can prevent RGC death in optic neuropathies; therefore, the identification of new targets for supporting RGC survival is crucial in the development of novel treatments for eye diseases. In this study we identify that the receptor tyrosine kinase, Tyro3, is critical for normal neuronal function in the adult mouse retina. The loss of Tyro3 results in a reduction in photoreceptor and RGC function as measured using electroretinography. The reduction in RGC function was associated with a thinner retinal nerve fiber layer and fewer RGCs. In the central retina, independent of the loss of RGCs, Tyro3 deficiency resulted in a dramatic reduction in the number of RGC dendrites in the inner plexiform layer. Our results show that Tyro3 has a novel, previously unidentified role in retinal function, RGC survival and RGC morphology. The Tyro3 pathway could therefore provide an alternative, targetable pathway for RGC protective therapeutics.
Notes on some family names of florideophyceae rhodophyta

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Applying big data based deep learning system to intrusion detection

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world's internet infrastructures, a machine learning based Intrusion Detection Systems (IDS) has become a vital component to protect our economic and national security. Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection. The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns. Particularly, the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples. In order to further enhance the performance of machine learning based IDS, we propose the Big Data based Hierarchical Deep Learning System (BDHDLS). BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload. Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster. This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches. Based on parallel training strategy and big data techniques, the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.
Isolation and characterization of acetic acid bacteria in cocoa fermentation

Acetic acid bacteria (AAB) from traditional indigenous process of cocoa bean fermentation were studied at a location in Huimanguillo, Tabasco, Mexico. High counts were detected at the beginning of the fermentation (5.7+ or - 0.26 cfu/g dry matter) and were present throughout fermentation time. Six AAB strains were isolated and characterized by morphological, biochemical and molecular methods. Morphological and biochemical methods were inconclusive for final identification of bacteria. However, the amplification of16S rDNA of the strains by polymerase chain reaction (PCR) and the Blast analysis of the obtained nucleotide sequences showed homology (99 to 100%) with Acetobacter tropicalis. Because the production of acetic acid is an important factor in the development of chocolate flavour precursors we studied the capacities of the strains to produce acetic acid. A. tropicalis ITV61 reached 2.5% (v/v) acetic acid production in 9-h on potato broth medium supplemented with ethanol (4% v/v) while the others A. tropicalis strains accumulated low acetic acid concentrations (> 0.2%). A. tropicalis ITV61 strain was able to grow at 4, and 7% ethanol. Tolerance of the strain A. tropicalis ITV61 to acetic acid is not affected at concentrations of 1% from the beginning of fermentation. Key words: Acetobacter, cocoa, fermentation, isolation, molecular characterization.
B3-Tree

In this work, we propose B3-tree, a hybrid index for persistent memory that leverages the byte-addressability of the in-memory index and the page locality of B-trees. As in the byte-addressable in-memory index, B3-tree is updated by 8-byte store instructions. Also, as in disk-based index, B3-tree is failure-atomic since it makes every 8-byte store instruction transform a consistent index into another consistent index without the help of expensive logging. Since expensive logging becomes unnecessary, the number of cacheline flush instructions required for B3-tree is significantly reduced. Our performance study shows that B3-tree outperforms other state-of-the-art persistent indexes in terms of insert and delete performance. While B3-tree shows slightly worse performance for point query performance, the range query performance of B3-tree is 2x faster than FAST and FAIR B-tree because the leaf page size of B3-tree can be set to 8x larger than that of FAST and FAIR B-tree without degrading insertion performance. We also show that read transactions can access B3-tree without acquiring a shared lock because B3-tree remains always consistent while a sequence of 8-byte write operations are making changes to it. As a result, B3-tree provides high concurrency level comparable to FAST and FAIR B-tree.
A hybrid brain-computer interface for closed-loop position control of a robot arm

Brain-Computer interfacing ( BCI ) has currently added a new dimension in assistive robotics. Existing brain-computer interfaces designed for position control applications suffer from two fundamental limitations. First, most of the existing schemes employ open-loop control, and thus are unable to track positional errors, resulting in failures in taking necessary online corrective actions. There are examples of a few works dealing with closed-loop electroencephalography ( EEG ) -based position control. These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule, which often creates a bottleneck preventing time-efficient control. Second, the existing brain-induced position controllers are designed to generate a position response like a traditional first-order system, resulting in a large steady-state error. This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential ( SSVEP ) induced link-selection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots / undershoots to reduce steady-state errors. Other than the above, the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors. Experiments undertaken reveal that the steady-state error is reduced to 0.2 % . The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.
Geolocation Correction for Geostationary Satellite Observations by a Phase-Only Correlation Method Using a Visible Channel

This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA’s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.
The Wall: The Earth in True Natural Color from Real-Time Geostationary Satellite Imagery

We present “The Wall”, the first web-based platform that animates the Earth in true natural color and close to real-time. The living planet is displayed both during day and night with a pixel resolution of approximately 1 km and a time frequency of 10 min. The automatic processing chains use the synchronized measurements provided by three geostationary satellites: the METEOSAT Second Generation (MSG2), Himawari-8, and GOES-16. A Rayleigh scattering correction is applied, and a cloud of artificial neural networks, chosen to render “true natural color” RBG composites, is used to recreate the missing daytime bands in the visible spectrum. The reconstruction methodology is validated by means of the TERRA/AQUA “Moderate Resolution Imaging Spectroradiometer” (MODIS) instrument reflectance values. “The Wall” is a dynamic broadcasting platform from which the scientific community and the public can trace local and Earth-wide phenomena and assess their impact on the globe.
ResIoT: An IoT social framework resilient to malicious activities

The purpose of the next internet of things ( IoT ) is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting. The convergence of IoT and multi-agent systems ( MAS ) provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine ( M2M ) cooperation among smart entities. However, the selection of reliable partners for cooperation represents a hard task in a mobile and federated context, especially because the trustworthiness of devices is largely unreferenced. The issues discussed above can be synthesized by recalling the well known concept of social resilience in IoT systems, i.e., the capability of an IoT network to resist to possible attacks by malicious agent that potentially could infect large areas of the network, spamming unreliable information and / or assuming unfair behaviors. In this sense, social resilience is devoted to face malicious activities of software agents in their social interactions, and do not deal with the correct working of the sensors and other information devices. In this setting, the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities. In this paper, we propose a framework for agents operating in an IoT environment, called ResIoT, where the formation of communities for collaborative purposes is performed on the basis of agent reputation. In order to validate our approach, we performed an experimental campaign by means of a simulated framework, which allowed us to verify that, by our approach, devices have not any economic convenience to performs misleading behaviors. Moreover, further experimental results have shown that our approach is able to detect the nature of the active agents in the systems ( i.e., honest and malicious ) , with an accuracy of not less than 11 % compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.