Off-grid sparse Bayesian learning algorithms for estimating the directions-of-arrival (DOAs) of multiple signals using an array of sensors are attractive in practice due to three primary reasons. ...First, these algorithms are fully automatic Bayesian algorithms and hence tuning of regularization parameters (hyperparameters) is not necessary. Second, since these algorithms are based on sparsity, they can produce high accuracy DOA estimates by exploiting the spatial sparsity of acoustic signals even when the signals are coherent. Third, they can also estimate the offset in the DOAs for signals, whose DOAs are not exactly aligned with the steering vectors. Two previously proposed off-grid sparse Bayesian DOA estimation algorithms are considered. The first off-grid model is based on the Taylor series expansion method (OGSBL-T algorithm) and the second is based on the linear interpolation method (OGSBL-I algorithm). The Cramer-Rao lower bound (CRLB) of the off-grid bias parameters for both the algorithms is derived for multiple snapshots. It is shown that the CRLB of the off-grid bias parameters for the OGSBL-T algorithm is significantly less than that for the OGSBL-I algorithm. It is also shown that the CRLBs of the off-grid bias parameters for both the algorithms get worse when we move from the broadside to the endfire directions. A simulation study is also carried out to characterize the performances of both the algorithms in terms of the root-meansquared error in the DOA estimates. It is shown that the OGSBL-T algorithm performs comparably to the OGSBL-I algorithm when the signals are relatively broadside and better than the OGSBL-I algorithm when the signals are relatively endfire. Finally, the application of the OGSBL-T algorithm for high resolution DOA estimation in an underwater communication system is demonstrated by analyzing passive sonar data from the SWellEx-96 ocean acoustic experiment.
Investigations using noninvasive functional magnetic resonance imaging (fMRI) have provided significant insights into the unique functional organization and profound importance of the human default ...mode network (DMN), yet these methods are limited in their ability to resolve network dynamics across multiple timescales. Electrophysiological techniques are critical to address these challenges, yet few studies have explored the neurophysiological underpinnings of the DMN. Here we investigate the electrophysiological organization of the DMN in a common large-scale network framework consistent with prior fMRI studies. We used intracranial EEG (iEEG) recordings, and evaluated intra- and cross-network interactions during resting-state and its modulation during a cognitive task involving episodic memory formation. Our analysis revealed significantly greater intra-DMN phase iEEG synchronization in the slow-wave (< 4 Hz), while DMN interactions with other brain networks was higher in the beta (12–30 Hz) and gamma (30–80 Hz) bands. Crucially, slow-wave intra-DMN synchronization was observed in the task-free resting-state and during both verbal memory encoding and recall. Compared to resting-state, slow-wave intra-DMN phase synchronization was significantly higher during both memory encoding and recall. Slow-wave intra-DMN phase synchronization increased during successful memory retrieval, highlighting its behavioral relevance. Finally, analysis of nonlinear dynamic causal interactions revealed that the DMN is a causal outflow network during both memory encoding and recall. Our findings identify frequency specific neurophysiological signatures of the DMN which allow it to maintain stability and flexibility, intrinsically and during task-based cognition, provide novel insights into the electrophysiological foundations of the human DMN, and elucidate network mechanisms by which it supports cognition.
•Geological mapping of vegetation-covered NFTB is done with ALOS-2 SAR data.•Second order lithological variance could be discriminated by this process.•Meso-scaled geological lineaments has been ...mapped and analysed.
In this study statics-based textural analysis GLCM (Grey Level Co-occurrence Matrix) of high resolution L band SAR data from ALOS-2 of JAXA has been used to discriminate between lithological facies of the fold-thrust belt of Nagaland, India. Sandstones, shales, siltstones and clays have been separated in the analysis. Importantly, the lithological facies with second-order compositional variance have been distinguished from each other. The study of 27 lithological pairs indicates a Transformed Divergence index greater than 1.7 except one pair which shows an index of 1.6. The average GLCM texture values of different stratigraphic units in VV and VH polarization shows appreciable differences. Coefficient of variation of the texture samples from each descriptor show uniform distribution around their mean. The mean has thus been used as a lithological index in this study. Further, the medium scaled geological lineaments have been extracted and mapped at 1: 25,000 scale. The Compass Edge Detector on the first Principal Component of full polarized ALOS-2 scene has been used for this purpose. Fracture network, outcrop length, density, and orientation have also been analyzed. The study is successful in rock characterization within vegetation-covered landscape, using L band SAR remote sensing. It may provide an important tool in geological mapping in inaccessible terrains with limited ground data control. The fracture map and its study can provide some useful insights about reservoir characterization and reduce exploration risk in this terrain that has a proven petroleum system.
Indiscriminate use of chemical fertilizers in the agricultural production systems to keep pace with the food and nutritional demand of the galloping population had an adverse impact on ecosystem ...services and environmental quality. Hence, an alternative mechanism is to be developed to enhance farm production and environmental sustainability. A nanohybrid construct like nanofertilizers (NFs) is an excellent alternative to overcome the negative impact of traditional chemical fertilizers. The NFs provide smart nutrient delivery to the plants and proves their efficacy in terms of crop productivity and environmental sustainability over bulky chemical fertilizers. Plants can absorb NFs by foliage or roots depending upon the application methods and properties of the particles. NFs enhance the biotic and abiotic stresses tolerance in plants. It reduces the production cost and mitigates the environmental footprint. Multitude benefits of the NFs open new vistas towards sustainable agriculture and climate change mitigation. Although supra-optimal doses of NFs have a detrimental effect on crop growth, soil health, and environmental outcomes. The extensive release of NFs into the environment and food chain may pose a risk to human health, hence, need careful assessment. Thus, a thorough review on the role of different NFs and their impact on crop growth, productivity, soil, and environmental quality is required, which would be helpful for the research of sustainable agriculture.
Display omitted
•Nanofertilizers (NFs) are the best alternative to traditional chemical fertilizers.•Nutrients use efficiency of NFs is higher than the conventional chemical fertilizers.•NFs can increase the tolerance of plants against biotic and abiotic stresses.•Supra optimal dose of NFs had a negative impact on crops, soil, and the environment.
Neuromorphic architectures implement biological neurons and synapses to execute machine learning algorithms with spiking neurons and bio-inspired learning algorithms. These architectures are energy ...efficient and therefore, suitable for cognitive information processing on resource and power-constrained environments, ones where sensor and edge nodes of internet-of-things (IoT) operate. To map a spiking neural network (SNN) to a neuromorphic architecture, prior works have proposed design-time based solutions, where the SNN is first analyzed offline using representative data and then mapped to the hardware to optimize some objective functions such as minimizing spike communication or maximizing resource utilization. In many emerging applications, machine learning models may change based on the input using some online learning rules. In online learning, new connections may form or existing connections may disappear at run-time based on input excitation. Therefore, an already mapped SNN may need to be re-mapped to the neuromorphic hardware to ensure optimal performance. Unfortunately, due to the high computation time, design-time based approaches are not suitable for remapping a machine learning model at run-time after every learning epoch. In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at run-time. Our design methodology operates in two steps – step 1 is a layer-wise greedy approach to partition SNNs into clusters of neurons and synapses incorporating the constraints of the neuromorphic architecture, and step 2 is a hill-climbing optimization algorithm that minimizes the total spikes communicated between clusters, improving energy consumption on the shared interconnect of the architecture. We conduct experiments to evaluate the feasibility of our algorithm using synthetic and realistic SNN-based applications. We demonstrate that our algorithm reduces SNN mapping time by an average 780x compared to a state-of-the-art design-time based SNN partitioning approach with only 6.25% lower solution quality.
Hepatic and biliary ascariasis Das, Anup K
Journal of global infectious diseases,
04/2014, Letnik:
6, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Ascariasis mainly contributes to the global helminthic burden by infesting a large number of children in the tropical countries. Hepato-biliary ascariasis (HBA) is becoming a common entity now than ...in the past owing to the frequent usage of ultrasonograms and endoscopic diagnostic procedures in the clinical practice. There are a variety of manifestations in HBA and diagnosis depends on a high index of suspicion in endemic areas coupled with subsequent confirmation by sonographic or endoscopic demonstration of the worm. Most of them present with acute abdomen and jaundice. Oriental or recurrent pyogenic cholangiopathy is possibly the result of HBA, commonly encountered in South-East Asian countries. Conservative treatment with anthelminthic agents is used in the majority. Failure to respond to medical therapy usually indicates the need for endoscopic or surgical interventions. Overall, mortality is low and prognosis is good, but many epidemiological and immunological aspects of Ascaris infection are unclear, meaning our understanding the disease and infection still remains incomplete. Therefore, it is difficult to definitely put down a fixed modality of treatment for HBA. This underscores the need for further studies as ascariasis has the potential to adversely affect the national socio-economy by compromising the health of children and adults alike with its sheer number.
•Three deep learning model have been assessed for automated blood smear analysis.•A generated dataset is used to perform this study.•Analysis is being performed to compare three deep learning model ...namely Faster R-CNN.•EfficientDet D3, and CenterNet Hourglass.•The best model observed is Faster R-CNN with 99.4 % average precision.
A blood smear is a common type of blood test where blood sample is taken from a patient, smear is made from the sample followed by observation of red blood cells, white blood cells and platelets. A pathologist carefully observes the sample and manually counts the number of RBC, WBC and platelets. This entire process from creating a smear to manually counting each element is tedious and susceptible to human errors. That is why, with the advancement of deep learning, various object detection techniques have become useful for automating the process and mitigating human errors in blood smear analysis. This work presents a comparative assessment of three different object detection models namely Faster R-CNN, EfficientDet D3 and CenterNet Hourglass, and presents their respective inference results. The three models have been compared using the COCO evaluation metrics to identify the best model performance for the given task. It is observed that out of the three models, the Faster R-CNN model performs the best in detecting WBCs and platelets in microscopic blood smear images with an average precision of 99.4%. Critical tasks like medical image processing require accurate predictions to prevent unintended ramifications. Therefore, while slower in terms of inference time, Faster R-CNN is the go-to model where accuracy is the priority. The work is also compared with the existing work in this domain to prove its efficiency.
•A methodology developed for generalised image acquisition.•A benchmarked dataset generated with 42 original whole slide images.•The study was performed on 720 nuclei images automatically ...segmented.•Final classes reflect the Benign and Malignant cases.•SVM and Linear Discriminant classifier gave the best result (100 %) for texture and colour features respectively.
Despite profound knowledge of the incidence of oral cancers and a large body of research beyond it, it continues to beat diagnosis and treatment management. Post physical observation by clinicians, a biopsy is a gold standard for accurate detection of any abnormalities. Towards the application of artificial intelligence as an aid to diagnosis, automated cell nuclei segmentation is the most essential step for the recognition of the cancer cells. In this study, we have extracted the shape, texture and color features from the histopathological images collected indigenously from regional hospitals. A dataset of 42 whole slide slices was used to automatically segment and generate a cell level dataset of 720 nuclei. Next, different classifiers were applied for classification purposes. 99.4 % accuracy using Decision Tree Classifier, 100 % accuracy using both SVM and Logistic regression and 100 % accuracy using SVM, Logistic regression and Linear Discriminant were acquired for shape, textural and color features respectively. The in-depth analysis showed SVM and Linear Discriminant classifier gave the best result for texture and color features respectively. The achieved result can be effectively converted to software as an assistant diagnostic tool.
•Silver nanoparticle (SNP) loaded amorphous gel was developed for infected deep wounds.•SNPs were developed in situ without using any toxic reducing agent or organic solvent.•SNP–CMC exhibited high ...antibacterial activity against different strains including MRSA.
There is a growing demand for an appropriate and safe antimicrobial dressing to treat infected deep wounds. An amorphous gel formulation (SNP–CMC), containing silver nanoparticles (SNPs) and carboxymethylcellulose (CMC), was prepared in one step by the reduction of silver nitrate in situ. Spectrophotometric and microscopic analysis revealed that the SNPs were 7–21nm in diameter. In simulated wound experiments, SNP–CMC gel was found to absorb 80.48±4.69% w/w of saline and donate 17.43±0.76% w/w of moisture within 24h indicating its dual fluid affinity. Cytocompatibility of the gel was assessed by proliferation studies with primary human skin cells. The antimicrobial activity studies showed that SNP–CMC containing 50ppm of SNPs was effective against the growth of both Gram negative and Gram positive strains including methicillin-resistant Staphylococcus aureus (MRSA). These results indicate that SNP–CMC could be ideal for the treatment of deep infected wounds.
Background
There is on-going controversy regarding the potential for increased respiratory effort to generate patient self-inflicted lung injury (P-SILI) in spontaneously breathing patients with ...COVID-19 acute hypoxaemic respiratory failure. However, direct clinical evidence linking increased inspiratory effort to lung injury is scarce. We adapted a computational simulator of cardiopulmonary pathophysiology to quantify the mechanical forces that could lead to P-SILI at different levels of respiratory effort. In accordance with recent data, the simulator parameters were manually adjusted to generate a population of 10 patients that recapitulate clinical features exhibited by certain COVID-19 patients, i.e., severe hypoxaemia combined with relatively well-preserved lung mechanics, being treated with supplemental oxygen.
Results
Simulations were conducted at tidal volumes (VT) and respiratory rates (RR) of 7 ml/kg and 14 breaths/min (representing normal respiratory effort) and at VT/RR of 7/20, 7/30, 10/14, 10/20 and 10/30 ml/kg / breaths/min. While oxygenation improved with higher respiratory efforts, significant increases in multiple indicators of the potential for lung injury were observed at all higher VT/RR combinations tested. Pleural pressure swing increased from 12.0 ± 0.3 cmH
2
O at baseline to 33.8 ± 0.4 cmH
2
O at VT/RR of 7 ml/kg/30 breaths/min and to 46.2 ± 0.5 cmH
2
O at 10 ml/kg/30 breaths/min. Transpulmonary pressure swing increased from 4.7 ± 0.1 cmH
2
O at baseline to 17.9 ± 0.3 cmH
2
O at VT/RR of 7 ml/kg/30 breaths/min and to 24.2 ± 0.3 cmH
2
O at 10 ml/kg/30 breaths/min. Total lung strain increased from 0.29 ± 0.006 at baseline to 0.65 ± 0.016 at 10 ml/kg/30 breaths/min. Mechanical power increased from 1.6 ± 0.1 J/min at baseline to 12.9 ± 0.2 J/min at VT/RR of 7 ml/kg/30 breaths/min, and to 24.9 ± 0.3 J/min at 10 ml/kg/30 breaths/min. Driving pressure increased from 7.7 ± 0.2 cmH
2
O at baseline to 19.6 ± 0.2 cmH
2
O at VT/RR of 7 ml/kg/30 breaths/min, and to 26.9 ± 0.3 cmH
2
O at 10 ml/kg/30 breaths/min.
Conclusions
Our results suggest that the forces generated by increased inspiratory effort commonly seen in COVID-19 acute hypoxaemic respiratory failure are comparable with those that have been associated with ventilator-induced lung injury during mechanical ventilation. Respiratory efforts in these patients should be carefully monitored and controlled to minimise the risk of lung injury.
PDF
