Estuaries have been under sampled to establish them as sources or sinks of the atmospheric carbon dioxide. Such poor coverage is well known for tropical, particularly monsoon driven, estuaries. In an ...attempt to study the variability in CO2 in a tropical monsoon estuary we made systematic time‐series observations in the Gautami Godavari estuarine system in the east coast of India. Our 18 month‐long extensive monitoring in the tropical Godavari estuarine system revealed pH >7.8 during dry period that decreased by 1.5 ± 0.01 during peak discharge period. The decrease in pH was associated with high nutrients and bacterial activities suggesting significant organic carbon decomposition. High bacterial respiration (20.6 ± 7.2 μMC l−1 d−1) in the estuary resulted in very high pCO2 of ∼30,000 μatm during peak discharge period, which otherwise were <500 μatm during dry period. Such high pCO2 levels were unknown to occur in any aquatic region. Several major and minor estuaries flow into the northern Indian Ocean from the Indian subcontinent and the monsoon associated processes make these systems chimney for emitting CO2 to atmosphere unrealized hitherto.
Key Points
Tropical estuaries are significant source of CO2
Monsoonal river discharges bring acidic waters to estuary
High temporal variability in pCO2 levels in the estuary
Extracellular histones in tissue injury and inflammation Allam, Ramanjaneyulu; Kumar, Santhosh V. R.; Darisipudi, Murthy N. ...
Journal of molecular medicine (Berlin, Germany),
05/2014, Letnik:
92, Številka:
5
Journal Article
Recenzirano
Neutrophil NETosis is an important element of host defense as it catapults chromatin out of the cell to trap bacteria, which then are killed, e.g., by the chromatin’s histone component. Also, during ...sterile inflammation TNF-alpha and other mediators trigger NETosis, which elicits cytotoxic effects on host cells. The same mechanism should apply to other forms of regulated necrosis including pyroptosis, necroptosis, ferroptosis, and cyclophilin D-mediated regulated necrosis. Beyond these toxic effects, extracellular histones also trigger thrombus formation and innate immunity by activating Toll-like receptors and the NLRP3 inflammasome. Thereby, extracellular histones contribute to the microvascular complications of sepsis, major trauma, small vessel vasculitis as well as acute liver, kidney, brain, and lung injury. Finally, histones prevent the degradation of extracellular DNA, which promotes autoimmunization, anti-nuclear antibody formation, and autoimmunity in susceptible individuals. Here, we review the current evidence on the pathogenic role of extracellular histones in disease and discuss how to target extracellular histones to improve disease outcomes.
Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost ...effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.
Gold nanoparticles of 20−100 nm diameter were synthesized within HEK-293 (human embryonic kidney), HeLa (human cervical cancer), SiHa (human cervical cancer), and SKNSH (human neuroblastoma) cells. ...Incubation of 1 mM tetrachloroaurate solution, prepared in phosphate buffered saline (PBS), pH 7.4, with human cells grown to ∼80% confluency yielded systematic growth of nanoparticles over a period of 96 h. The cells, stained due to nanoparticle growth, were adherent to the bottom of the wells of the tissue culture plates, with their morphology preserved, indicating that the cell membrane was intact. Transmission electron microscopy of ultrathin sections showed the presence of nanoparticles within the cytoplasm and in the nucleus, the latter being much smaller in dimension. Scanning near field microscopic images confirmed the growth of large particles within the cytoplasm. Normal cells gave UV−visible signatures of higher intensity than the cancer cells. Differences in the cellular metabolism of cancer and noncancer cells were manifested, presumably in their ability to carry out the reduction process.
11•Changes in the composition of waste generated during COVID-19 presents considerable new challenges.11•Ensuring safe waste management practices should be a part of emergency response services ...during COVID-19 crisis.11•Temporary relaxation on use of single-use plastic during COVID-19 crises could impact consumer's behavior.11•Shift to automated waste treatment systems will reduce the risk of transmission.11•Building localized robust supply chains could help fight possible future pandemics.
The crisis brought upon by the COVID-19 pandemic has altered global waste generation dynamics and therefore has necessitated special attention. The unexpected fluctuations in waste composition and quantity also require a dynamic response from policymakers. This study highlights the challenges faced by the solid waste management sector during the pandemic and the underlying opportunities to fill existing loopholes in the system. The study presents specific cases for biomedical waste, plastic waste, and food waste management - all of which have been a major cause of concern during this crisis. Further, without active citizen participation and cooperation, commingled virus-laden biomedical waste with the regular solid waste stream pose significant negative health and safety issues to sanitation workers. Single-use plastic usage is set to bounce back due to growing concerns of hygiene, particularly from products used for personal protection and healthcare purposes. It is expected that household food waste generation may reduce due to increased conscious buying of more non-perishable items during lockdown and due to concerns of food shortage. However, there is a chance of increase in food waste from the broken supply chains such as food items getting stuck on road due to restriction in vehicle movements, lack of workers in the warehouse for handling the food products, etc. The study also stresses the need for building localized resilient supply chains to counter such situations during future pandemics. While offering innovative solutions to existing waste management challenges, the study also suggests some key recommendations to the policymakers to help handle probable future pandemics if any holistically.
In this work, the electrochemical performance of NiFe2O4 nanofibers synthesized by an electrospinning approach have been discussed in detail. Lithium storage properties of nanofibers are evaluated ...and compared with NiFe2O4 nanoparticles by galvanostatic cycling and cyclic voltammetry studies, both in half-cell configurations. Nanofibers exhibit a higher charge-storage capacity of 1000 mAh g–1 even after 100 cycles with high Coulmbic efficiency of 100 % between 10 and 100 cycles. Ex situ microscopy studies confirmed that cycled nanofiber electrodes maintained the morphology and remained intact even after 100 charge–discharge cycles. The NiFe2O4 nanofiber electrode does not experience any structural stress and eventual pulverisation during lithium cycling and hence provides an efficient electron conducting pathway. The excellent electrochemical performance of NiFe2O4 nanofibers is due to the unique porous morphology of continuous nanofibers.
In the semi-arid plains of Southern India, outside the protected area network, sacred groves forests and the barren lands invaded by Prosopis juliflora are reckoned to be the major greenery, but have ...homogenous and heterogeneous vegetation respectively. This study attempted to compare 50 Sacred Groves Stands (SGS) and 50 monodominant Prosopis juliflora Stands (PJS) for the functional diversity, evenness, floral diversity, carbon stock and dynamics, carbon-fixing traits, dendrochronology of trees, soil nutrient profiles, and soil erosion. Quadrat sample survey was adopted to record stand density, species richness, abundance, basal area and leaf area index; composite soil samples were collected at depths 0-30 cm for nutrient profiling (N, P, K, and OC). Photosynthesis rate (µmole co
m
/sec), air temperature (°c), leaf intracellular co
concentration (ppm), ambient photosynthetic active radiation (µmole m
/sec), transpiration rate (m. mole H
O m
/sec) were determined for the 51 tree species existed in SGS and PJS using Plant Photosynthesis system. Structural Equation Model (SEM) was applied to derive the carbon sequestering potential and photosynthetic efficiency of eight dominant tree species using vital input parameters, including eco-physiological, morphological, and biochemical characterization. The Revised Universal Soil Loss Equation (RUSLE) model, in conjunction with ArcGIS Pro and ArcGIS 10.3, was adopted to map soil loss. Carbon source/sink determinations inferred through Net Ecosystem Productivity (NEP) assessments showed that mature SGS potentially acted as a carbon sink (0.06 ± 0.01 g C/m
/day), while matured PJS acted as a carbon source (-0.34 ± 0.12 g C/m
/day). Soil erosion rates were significantly greater (29.5 ± 13.4 ton/ha/year) in SGS compared to PJS (7.52 ± 2.55 ton/ha/year). Of the eight selected tree species, SEM revealed that trees belonging to the family Fabaceae Wrightia tinctoria (estimated coefficient: 1.28, p = 0.02) > Prosopis juliflora (1.22, p = 0.01) > Acacia nilotica (1.21, p = 0.03) > Albizia lebbeck (0.97, p = 0.01) showed comparatively high carbon sequestering ability.
Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach ...for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.
Mobile learning when stored in a cloud server allows contents to be gathered and accessed using mobile devices connected with the cloud. The present problems of limited computing capacity and small ...space for storage in mobile phones has inspired the blend of mobile learning and cloud computing. This paper primarily focuses on Homomorphic Encryption to achieve privacy over encoded data or search the encrypted information, which is the current research area of majority of the knowledge experts. In this paper, we suggest Shifted Adaption Homomorphism Encryption (SAHE), which is regarded as the better option for all the current research going on. SAHE implements the smallest public key of 32 bit and is able to encrypt integer and real numbers. A major issue in this field of research is difficulty in protecting user's questions, which is addressed by conceiving a public key encryption technique which is based on the reversed index. Our schema preserves search efficiency using inverted index, by solving one time only search drawback encountered in earlier research works. This method is appropriate for mobile learning since the suggested algorithm will not use the mobile memory or power.
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