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Applying organic amendments and bioagents is considered an ecofriendly practice to promote soil fertility and suppressiveness against many pathogens. This study determined the suppressiveness of seven different organic amendments and four bioagents against the cumin wilt disease at the Department of Plant Pathology, College of Agriculture, Jodhpur (Agriculture University Jodhpur) in the years 2020-2021. Among them, the lowest incidence was recorded in mustard cake (27.11%), neem cake (29.37%), groundnut cake (29.96%), and sesame cake (31.81%). Disease incidence was higher in other amendment applications like castor cake (33.21%) and cotton cake (33.14%). However, the disease incidence was much higher in controls (64.11%). Among bioagent treatments in vitro, Trichoderma harzianum (78.25%) and Trichoderma viride (72.69%) were the best at inhibiting the mycelial growth of Fusarium oxysporum f. spp. cumini compared to the treatment of Bacillus subtilis (55.34%). [ABSTRACT FROM AUTHOR]

Background: Periapical granuloma and periapical cysts develop as a sequela of inflammation of dental pulp, usually a consequence of an infected root canal. Mast cells (MCs) have most frequently been implicated in the pathophysiology of periapical lesions such as periapical granulomas and cysts. Aims: The present study was undertaken to emphasize the presence and quantification of MCs in periapical lesions (granulomas and cysts) using toluidine blue stain, with an aim to elucidate their role in the pathogenesis of these lesions. Materials and Methods: The study comprised a total of 55 previously diagnosed cases (30 cases of radicular cyst and 25 cases of periapical granuloma) selected from the departmental archives. The MCs were quantified in all the sections in the three zones, namely the epithelial, subepithelial, and deeper connective tissue zones. The results were sent for statistical analysis. Statistical Analysis: Wilcoxon paired t-test and Mann–Whitney U test were applied for intra- and intergroup comparison MCs in different zones of periapical granulomas and radicular cyst, respectively. Results: A statistically significant difference was observed between the two lesions in all the three zones, i.e., epithelial, subepithelial, and in deeper connective tissue zone with P = 0.001. Conclusion: Although MCs were present in all the three zones of both periapical cysts and granuloma, they were predominantly observed in radicular cysts than in periapical granuloma. This suggests a significant role of MC in initiation, development, and progression of these periapical lesions. [ABSTRACT FROM AUTHOR]

Groundnut is an economic important edible oilseed crop. Collar rot is one of the most destructive diseases of groundnut. The use of fungicides to manage disease in crops causes much adverse health and environmental effects; therefore, the need for a more sustainable and ecological approach to control crop diseases without chemical fungicides becomes inevitable. Accordingly, a viable alternative method was used. Seven plant extracts and five bio-agents were evaluated by poison food technique and dual culture technique under laboratory conditions against Aspergillus niger in 2019-2020. Among them, datura leaf extract was most effective and inhibited mycelial growth by 84.06%, followed by tulsi (79.18%) and neem seed kernel extract (77.05%) when applied at 10% concentration of the extracts. The least inhibition was observed due to Salvadora extract (35.85%). Among bio-agents, Trichoderma harzianum inhibited mycelial growth by 77.67%, followed by Trichoderma viride (72.92%) in vitro. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to assess the efficacy of nine phytoextracts and four oil cakes against the pathogen Alternaria solani, causing early blight of tomatoes. Among the phytoextracts, Azadirachta indica at 30% concentration was most effective and inhibited mycelial growth by 74.67%, followed by Pongamia pinnata (65.64%), Ocimum sanctum (57.74%), Eucalyptus globulus (56.53%), and Jatropha curcas (54.64%) after 120 h of inoculation. In the case of oil cakes, maximum inhibition (51.51%) was recorded after neem cake-based preparations, followed by mustard (44.56%) and cotton (42.75%). Minimum inhibition (34.48%) after 120 h was recorded when linseed cake-based product was used. [ABSTRACT FROM AUTHOR]

Background: Cheiloscopy is the study of furrows and grooves present on the red part, or the vermilion border of the human lips. The type of groves is unique for an individual and can be used for person identification. Aim: The aim of this study is to ascertain whether the uniqueness of lip print (LP) can help in identifying a person. In addition to evaluate the comparison of LP types and blood groups was taken up to see if there is any correlation between the two. Materials and Methods: A cross-sectional study was conducted on 200 participants (77 males and 123 females), aged between 25 to 45 years, to determine any correlation between LP types and blood groups. These lip patterns were analyzed and interpreted using Tsuchihashi's classification and later associated blood group matching was performed to determine the predominant LP type with the ABO and Rh blood grouping system. Results: The present study showed a positive correlation between LP pattern and blood group among males and females. Thus, distribution of LP patterns, and ABO blood groups might help in the identification of an individual. Conclusion: Since LP and blood group are unique for a person. Hence, cheiloscopy along with blood group can play a vital role in person identification. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, and face recognition, among others. However, deep CNNs demand substantial compute resources during training and inference. The machine learning community has mainly focused on model-level optimizations such as architectural compression of CNNs, whereas the system community has focused on implementation-level optimization. In between, various arithmetic-level optimization techniques have been proposed in the arithmetic community. This article provides a survey on resource-efficient CNN techniques in terms of model-, arithmetic-, and implementation-level techniques, and identifies the research gaps for resource-efficient CNN techniques across the three different level techniques. Our survey clarifies the influence from higher- to lower-level techniques based on our resource efficiency metric definition and discusses the future trend for resource-efficient CNN research. [ABSTRACT FROM AUTHOR]

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of attention lately due to its promise of reducing the computational energy, latency, as well as learning complexity in artificial neural networks. Taking inspiration from neuroscience, this interdisciplinary field performs a multi-stack optimization across devices, circuits, and algorithms by providing an end-to-end approach to achieving brain-like efficiency in machine intelligence. On one side, neuromorphic computing introduces a new algorithmic paradigm, known as Spiking Neural Networks (SNNs), which is a significant shift from standard deep learning and transmits information as spikes (“1” or “0”) rather than analog values. This has opened up novel algorithmic research directions to formulate methods to represent data in spike-trains, develop neuron models that can process information over time, design learning algorithms for event-driven dynamical systems, and engineer network architectures amenable to sparse, asynchronous, event-driven computing to achieve lower power consumption. On the other side, a parallel research thrust focuses on development of efficient computing platforms for new algorithms. Standard accelerators that are amenable to deep learning workloads are not particularly suitable to handle processing across multiple timesteps efficiently. To that effect, researchers have designed neuromorphic hardware that rely on event-driven sparse computations as well as efficient matrix operations. While most large-scale neuromorphic systems have been explored based on CMOS technology, recently, Non-Volatile Memory (NVM) technologies show promise toward implementing bio-mimetic functionalities on single devices. In this article, we outline several strides that neuromorphic computing based on spiking neural networks (SNNs) has taken over the recent past, and we present our outlook on the challenges that this field needs to overcome to make the bio-plausibility route a successful one. [ABSTRACT FROM AUTHOR]

Aim: The aim is to improve and develop a written digit identification to detect practically important issues in pattern recognition using Novel Convolution Neural Networks compared with Decision tree. Materials and Methods: Handwritten digit recognition is performed using Novel Convolution neural networks algorithm Sample size is 66 over decision tree algorithm Sample size is (N=66) with the split size using Table 1 and training using G power 80% and testing dataset 70% and 30% respectively. Results: The retrieval accuracy of the Novel Convolution Neural Networks classifier is (96.42%) and Decision tree is (72.35%), There exists a statistically insignificant difference between the two groups (p=0.193; p>0.05)`. Conclusion: The work has confirmed that the efficiency of the Novel convolution neural network algorithm has given more accuracy value in written digit identification when compared to decision tree algorithm using artificial intelligence algorithm. [ABSTRACT FROM AUTHOR]