769 research outputs found

    ACC-deaminase and/or nitrogen-fixing rhizobacteria and growth response of tomato (Lycopersicon pimpinellfolium Mill.)

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    The study aimed to identify and select important plant growth-promoting rhizobacteria (PGPR) and examine the response of tomato growth upon inoculation. Inoculation with rhizobacterial isolates increased all the measured physical, chemical, and enzymatic growth parameters compared to control. However, the TAN1 isolate had the highest effect, and significantly (P < 0.05) increased the root length (8.25-fold), root fresh (8.36-fold) and dry (12.6-fold) weight, shoot length (6.92-fold), shoot fresh (7.18-fold) and dry (6.90-fold) weight, number of leaves (11.0-fold), chlorophyll a (6.25-fold), chlorophyll b (10.7-fold), carotenoid contents (8.80-fold), seedlings fresh (9.0-fold) and dry (8.71-fold) weight, plant macronutrient uptake, i.e. N (7.7- and 8.9-fold), P (10.5- and 11.4-fold), K (7.8- and 8.8-fold), Ca (12.7- and 8.2-fold), and Mg (12.6- and 9-fold) in shoot and root, plant micronutrient uptake, i.e. Zn (6.6-, 10.2-), Cu (9.3-, and 10.3-fold), Fe (7.7- and 10.7-fold), and Mn (4.7- and 5.7-fold) in shoot and root and plant antioxidant enzymes, i.e. glutathione S-transferase (10.7-fold), peroxidase (8.1-fold), and catalase (10.5-fold). Our results concluded that inoculation of agricultural crops with rhizobacteria is a very useful approach to increase the plant growth. The rhizobacteria having both 1-aminocyclopropane-1-carboxylate (ACC) deaminase and nitrogen-fixing activity are more effective than rhizobacteria possessing either ACC-deaminase or nitrogen-fixing activity alone for growth promotion of crops

    Limbs detection and tracking of head-fixed mice for behavioral phenotyping using motion tubes and deep learning

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    The broad accessibility of affordable and reliable recording equipment and its relative ease of use has enabled neuroscientists to record large amounts of neurophysiological and behavioral data. Given that most of this raw data is unlabeled, great effort is required to adapt it for behavioral phenotyping or signal extraction, for behavioral and neurophysiological data, respectively. Traditional methods for labeling datasets rely on human annotators which is a resource and time intensive process, which often produce data that that is prone to reproducibility errors. Here, we propose a deep learning-based image segmentation framework to automatically extract and label limb movements from movies capturing frontal and lateral views of head-fixed mice. The method decomposes the image into elemental regions (superpixels) with similar appearance and concordant dynamics and stacks them following their partial temporal trajectory. These 3D descriptors (referred as motion cues) are used to train a deep convolutional neural network (CNN). We use the features extracted at the last fully connected layer of the network for training a Long Short Term Memory (LSTM) network that introduces spatio-temporal coherence to the limb segmentation. We tested the pipeline in two video acquisition settings. In the first, the camera is installed on the right side of the mouse (lateral setting). In the second, the camera is installed facing the mouse directly (frontal setting). We also investigated the effect of the noise present in the videos and the amount of training data needed, and we found that reducing the number of training samples does not result in a drop of more than 5% in detection accuracy even when as little as 10% of the available data is used for training

    Probing quench dynamics across a quantum phase transition into a 2D Ising antiferromagnet

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    Simulating the real-time evolution of quantum spin systems far out of equilibrium poses a major theoretical challenge, especially in more than one dimension. We experimentally explore the dynamics of a two-dimensional Ising spin system with transverse and longitudinal fields as we quench it across a quantum phase transition from a paramagnet to an antiferromagnet. We realize the system with a near unit-occupancy atomic array of over 200 atoms obtained by loading a spin-polarized band insulator of fermionic lithium into an optical lattice and induce short-range interactions by direct excitation to a low-lying Rydberg state. Using site-resolved microscopy, we probe the correlations in the system after a sudden quench from the paramagnetic state and compare our measurements to exact calculations in the regime where it is possible. We achieve many-body states with longer-range antiferromagnetic correlations by implementing a near-adiabatic quench and study the buildup of correlations as we cross the quantum phase transition at different rates

    Building energy metering and environmental monitoring - A state-of-the-art review and directions for future research

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    Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building energy metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that energy metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on energy metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent metering requirements in future, moving away from voluntary to mandatory compliance. With regards to metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for energy metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development – for use a reference, as well as for generating further interest in this expanding research area

    Ewing's sarcoma in a 52 year-old women with leg pain

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    Ewing's sarcoma (ES) is the second most common primary sacral tumor. ES are aggressive tumors with a tendency toward recurrence following resection and pronounced proclivity toward hematogenous metastasis to lungs and bone. A 52-year-old woman presented with of pain in her right posterior thigh radiating to the back of the knee. A magnetic resonance imaging showed an irregularly shaped right presacral mass. A core needle biopsy revealed a small, round blue cell neoplasm suggestive of a primitive neuroectodermal tumor
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