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Chain graphs and threshold graphs are special classes of graphs which have maximum spectral radius among bipartite graphs and connected graphs with given order and size, respectively. In this article, we focus on some of linear algebraic tools like rank, determinant, and permanent related to the adjacency matrix of these types of graphs. We derive results relating the rank and number of edges. We also characterize chain/threshold graphs with nonzero determinant and permanent.

Archana Bhat K,1 Deepak Madi,2 Sevitha Bhat,1 Therese Mary,3 Shalini Shenoy Mulki,1 Himani Kotian4 1Department of Microbiology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India; 2Department of Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India; 3Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India; 4Department of Community Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaCorrespondence: Sevitha Bhat, Department of Microbiology Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India, Tel +91 9449831631, Email sevitha.bhat@manipal.eduIntroduction: SARS CoV-2, a novel corona virus, has emerged in December 2019. The COVID-19 associated mortality is documented in elderly with co morbidities. To have better insight on this issue, the secondary bacterial infections with multi-drug-resistant bacteria in COVID-19 patients need to be studied to evaluate the impact of these infections on the outcome.Aim and objectives: To determine the proportion of secondary infections in COVID-19 patients. To study the spectrum of pathogens and antibiogram of the bacteria isolated from secondary infections in such patients. To evaluate the co-existing co-morbidities, treatment and outcome in these patients.Methodology: The retrospective study was conducted in Departments of Medicine and Microbiology, KMC hospitals Attavara and Ambedkar circle, Mangaluru, including all the hospitalized microbiologically confirmed cases of SARS CoV-2 infection. Details pertaining to the study population were collected using a structured proforma. Descriptive data were entered in the form of mean, median and proportions. The categorical values were analyzed using Chi square test. Values of p < 0.05 were considered as statistically significant.Results

Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.

The process of converting real-life high-quality pictures and videos into practical cartoon images and videos is known as cartoonization. The saved model decomposes uploaded images and videos into three different cartoon depictions as surface representation, structure representation, texture representation, which further instructs the network optimization to generate cartoon image. It helps to sleek the image, filter the qualities, transforming it to sketches, and translating the output from a domain to another. The extracted outputs are fed to a Generative Neural Networks (GAN) framework, which helps to improve our problem making the solution more flexible and varied, where GAN stands for Generative Adversarial Network is used to transform uploaded images (snapshots) to the finest cartooned image. Using the loss function and its two types named as Adversarial loss and Content Loss, we gained a flexible as well as a clear edge defined images.

Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.

The process of converting real-life high-quality pictures and videos into practical cartoon images and videos is known as cartoonization. The saved model decomposes uploaded images and videos into three different cartoon depictions as surface representation, structure representation, texture representation, which further instructs the network optimization to generate cartoon image. It helps to sleek the image, filter the qualities, transforming it to sketches, and translating the output from a domain to another. The extracted outputs are fed to a Generative Neural Networks (GAN) framework, which helps to improve our problem making the solution more flexible and varied, where GAN stands for Generative Adversarial Network is used to transform uploaded images (snapshots) to the finest cartooned image. Using the loss function and its two types named as Adversarial loss and Content Loss, we gained a flexible as well as a clear edge defined images.

The process of converting real-life high-quality pictures and videos into practical cartoon images and videos is known as cartoonization. The saved model decomposes uploaded images and videos into three different cartoon depictions as surface representation, structure representation, texture representation, which further instructs the network optimization to generate cartoon image. It helps to sleek the image, filter the qualities, transforming it to sketches, and translating the output from a domain to another. The extracted outputs are fed to a Generative Neural Networks (GAN) framework, which helps to improve our problem making the solution more flexible and varied, where GAN stands for Generative Adversarial Network is used to transform uploaded images (snapshots) to the finest cartooned image. Using the loss function and its two types named as Adversarial loss and Content Loss, we gained a flexible as well as a clear edge defined images.

Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.

The process of converting real-life high-quality pictures and videos into practical cartoon images and videos is known as cartoonization. The saved model decomposes uploaded images and videos into three different cartoon depictions as surface representation, structure representation, texture representation, which further instructs the network optimization to generate cartoon image. It helps to sleek the image, filter the qualities, transforming it to sketches, and translating the output from a domain to another. The extracted outputs are fed to a Generative Neural Networks (GAN) framework, which helps to improve our problem making the solution more flexible and varied, where GAN stands for Generative Adversarial Network is used to transform uploaded images (snapshots) to the finest cartooned image. Using the loss function and its two types named as Adversarial loss and Content Loss, we gained a flexible as well as a clear edge defined images.

Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.

Computer-Supported Cooperative Work (CSCW) and Human- Computer Interaction (HCI) have long studied how technology can support material and relational aspects of care work, typically in clinical healthcare settings. More recently, we see increasing recognition of care work such as informal healthcare provision, child and elderly care, organizing and advocacy, domestic work, and service work. However, the COVID-19 pandemic has underscored long-present tensions between the deep necessity and simultaneous devaluation of our care infrastructures. This highlights the need to attend to the broader social, political, and economic systems that shape care work and the emerging technologies being used in care work. This leads us to ask several critical questions: What counts as care work and why? How is care work (de)valued, (un)supported, or coerced under capitalism and to what end? What narratives drive the push for technology in care work and whom does it benefit? How does care work resist or build resilience against and within oppressive systems? And how can we as researchers advocate for and with care and caregivers? In this one-day workshop, we will bring together researchers from academia, industry, and community-based organizations to reflect on these questions and extend conversations on the future of technology for care work., Part of proceedings ISBN: 9781450384797QC 20220615

The process of converting real-life high-quality pictures and videos into practical cartoon images and videos is known as cartoonization. The saved model decomposes uploaded images and videos into three different cartoon depictions as surface representation, structure representation, texture representation, which further instructs the network optimization to generate cartoon image. It helps to sleek the image, filter the qualities, transforming it to sketches, and translating the output from a domain to another. The extracted outputs are fed to a Generative Neural Networks (GAN) framework, which helps to improve our problem making the solution more flexible and varied, where GAN stands for Generative Adversarial Network is used to transform uploaded images (snapshots) to the finest cartooned image. Using the loss function and its two types named as Adversarial loss and Content Loss, we gained a flexible as well as a clear edge defined images.

Handwritten digit recognition, an active research field of computer vision and pattern recognition. This paper proposes an efficient handwritten Kannada digit recognition approach based on comparing the prediction accuracy of the Convolutional Neural Network (CNN) model and various machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest Classifier (RFC) by training on Kannada-MNIST and testing on the Dig MNIST dataset. The Kannada language has complex digits, identifying such digits is a challenge in pattern recognition. In the end, a comparative study of the above-mentioned algorithms is performed based on recognition accuracy.

© 2019 Elsevier Ltd Internet of Things (IoT) becomes a new era of the Internet, which consists of several connected physical smart objects (i.e., sensing devices) through the Internet. IoT has different types of applications, such as smart home, wearable devices, smart connected vehicles, industries, and smart cities. Therefore, IoT based applications become the essential parts of our day-to-day life. In a cloud-based IoT environment, cloud platform is used to store the data accessed from the IoT sensors. Such an environment is greatly scalable and it supports real-time event processing which is very important in several scenarios (i.e., IoT sensors based surveillance and monitoring). Since some applications in cloud-based IoT are very critical, the information collected and sent by IoT sensors must not be leaked during the communication. To accord with this, we design a new lightweight authentication mechanism in cloud-based IoT environment, called LAM-CIoT. By using LAM-CIoT, an authenticated user can access the data of IoT sensors remotely. LAM-CIoT applies efficient “one-way cryptographic hash functions” along with “bitwise XOR operations”. In addition, fuzzy extractor mechanism is also employed at the user's end for local biometric verification. LAM-CIoT is methodically analyzed for its security part through the formal security using the broadly-accepted “Real-Or-Random (ROR)” model, formal security verification using the widely-used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool as well as the informal security analysis. The performance analysis shows that LAM-CIoT offers better security, and low communication and computation overheads as compared to the closely related authentication schemes. Finally, LAM-CIoT is evaluated using the NS2 network simulator for the measurement of network performance parameters that envisions the impact of LAM-CIoT on the network performance of LAM-CIoT and other schemes.

Bio -diversityrefers to the variations within the living world while genetic diversity is the sum of genetic characteristics within any species or genus. Genetic diversity serves as a way for populations to adapt to changing environments. With more variation, it is more likely that some individuals in a population will possess variations of alleles that are suited for the environment. Those individuals are more likely to survive to produce offspring bearing that allele. The population will continue for more generations because of the success of these individuals. The extensive literature survey revealed that Pongamia Pinnata L. is an important medicinal plant with diverse pharmacological spectrum. The plant shows the presence of many chemical constituents which are responsible for varied pharmacological and medicinal properties. Furthermore, it also represents a milestone in the field of biofuel industry as one of the most important bio fuel crop. The present investigation was carried out to assess the genetic diversity of 37 wild species of P. pinnata collected from different states from India based on random amplified polymorphic DNA (RAPD) and Sequenced Related Amplified Polymorphism (SRAP) primers. After screening a total of 18 RAPD and 23 SRAP primers were used which gave reproducible amplification banding patterns. In RAPD out of 157 alleles were generated of which 150 were polymorphic across the studied accessions (96.8% polymorphism). For SRAP the total number of amplified products was 145 of which 122 were polymorphic (84.1% polymorphism). The mean polymorphic information content (PIC) for both the markers was 0.77 and 0.68. Variation in combined Jaccard’s coefficient of similarity indicates the high level of genetic variation among the genotypes studied. The overall grouping pattern of clustering corresponds well with the principal component analysis (PCA), confirming patterns of genetic diversity observed among the accessions.Â

Bio -diversityrefers to the variations within the living world while genetic diversity is the sum of genetic characteristics within any species or genus. Genetic diversity serves as a way for populations to adapt to changing environments. With more variation, it is more likely that some individuals in a population will possess variations of alleles that are suited for the environment. Those individuals are more likely to survive to produce offspring bearing that allele. The population will continue for more generations because of the success of these individuals. The extensive literature survey revealed that Pongamia Pinnata L. is an important medicinal plant with diverse pharmacological spectrum. The plant shows the presence of many chemical constituents which are responsible for varied pharmacological and medicinal properties. Furthermore, it also represents a milestone in the field of biofuel industry as one of the most important bio fuel crop. The present investigation was carried out to assess the genetic diversity of 37 wild species of P. pinnata collected from different states from India based on random amplified polymorphic DNA (RAPD) and Sequenced Related Amplified Polymorphism (SRAP) primers. After screening a total of 18 RAPD and 23 SRAP primers were used which gave reproducible amplification banding patterns. In RAPD out of 157 alleles were generated of which 150 were polymorphic across the studied accessions (96.8% polymorphism). For SRAP the total number of amplified products was 145 of which 122 were polymorphic (84.1% polymorphism). The mean polymorphic information content (PIC) for both the markers was 0.77 and 0.68. Variation in combined Jaccard’s coefficient of similarity indicates the high level of genetic variation among the genotypes studied. The overall grouping pattern of clustering corresponds well with the principal component analysis (PCA), confirming patterns of genetic diversity observed among the accessions.Â

Concurrent activation of poly (ADP-ribose) polymerase (PARP) and DNA ligase was observed in cultured human epidermal keratinocytes (HEK) exposed to the DNA alkylating compound sulfur mustard (SM), suggesting that DNA ligase activation could be due to its modification by PARP. Using HEK, intracellular (3)H-labeled NAD+ ((3)H-adenine) was metabolically generated and then these cells were exposed to SM (1 mM). DNA ligase I isolated from these cells was not (3)H-labeled, indicating that DNA ligase I is not a substrate for (ADP-ribosyl)ation by PARP. In HEK, when PARP was inhibited by 3-amino benzamide (3-AB, 2 mM), SM-activated DNA ligase had a half-life that was four-fold higher than that observed in the absence of 3-AB. These results suggest that DNA repair requires PARP, and that DNA ligase remains activated until DNA damage repair is complete. The results show that in SM-exposed HEK, DNA ligase I is activated by phosphorylation catalysed by DNA-dependent protein kinase (DNA-PK). Therefore, the role of PARP in DNA repair is other than that of DNA ligase I activation. By using the DNA ligase I phosphorylation assay and decreasing PARP chemically as well as by PARP anti-sense mRNA expression in the cells, it was confirmed that PARP does not modify DNA ligase I. In conclusion, it is proposed that PARP is essential for efficient DNA repair: however, PARP participates in DNA repair by altering the chromosomal structure to make the DNA damage site(s) accessible to the repair enzymes., The original document contains color images. Pub. in Jnl. of Applied Toxicology, v26 p452-457, 2006.

DNA-dependent protein kinase (DNA-PK) phosphorylates several cellular proteins in vitro, but its cellular function and natural substrate(s) in vivo are not established. We reported activation of DNA ligase in cultured normal human epidermal keratinocytes (NHEK) on exposure to the DNA-damaging compound bis-(2-chloroethyl) sulfide. The activated enzyme was identified as DNA ligase I, and this activation was attributed to phosphorylation of the enzyme. Here, we show that the phosphorylation is mediated by DNA-PK and that DNA ligase I is one of its natural substrates in vivo. DNA ligase I phosphorylation-cum-activation is a response specific to DNA double-strand breaks. We also demonstrate that affinity-purified inactive DNA ligase I is phosphorylated and activated in vitro by HeLa Cell DNA-PK confirming the in vivo observations. The findings specify the roles of DNA-PK and DNA ligase I in mammalian DNA double-strand break repair., Published in Biochemistry, v45 n20 p6522-6528, 2006. The original document contains color images.

Objective: To study the effect of hydroalcoholic extract of Ziziphus jujuba leaves on neutrophil phagocytic function. Methods: The different concentrations (5, 10, 25, 50 and100 μg/ml) of Ziziphus jujuba leaves extract was subjected, to study its effect on different in vitro methods of phagocytosis such as neutrophil locomotion and chemotaxis test, in vitro immunostimulant activity by slide method and qualitative nitro blue tetrazolium test using human neutrophils. Results: The Ziziphus jujuba leaves extract has stimulated chemotactic, phagocytic and intracellular killing potency of human neutrophils at the concentration range of 5-50μg/ml. Conclusion: The hydroalcoholic extract of Ziziphus jujuba leaves stimulates cell-mediated immune system by increasing neutrophil phagocytic function.

Objective: To study the effect of hydroalcoholic extract of Ziziphus jujuba leaves on neutrophil phagocytic function. Methods: The different concentrations (5, 10, 25, 50 and100 μg/ml) of Ziziphus jujuba leaves extract was subjected, to study its effect on different in vitro methods of phagocytosis such as neutrophil locomotion and chemotaxis test, in vitro immunostimulant activity by slide method and qualitative nitro blue tetrazolium test using human neutrophils. Results: The Ziziphus jujuba leaves extract has stimulated chemotactic, phagocytic and intracellular killing potency of human neutrophils at the concentration range of 5-50μg/ml. Conclusion: The hydroalcoholic extract of Ziziphus jujuba leaves stimulates cell-mediated immune system by increasing neutrophil phagocytic function.

In cultured HEK, we observed that HD (0.3-1.0 mM)-induced DNA damage triggers DNA repair and apoptosis, which may be interdependent. We studied the effects of a general caspase inhibitor, Z-VAD fmk (0.004 mM) and the Fas (CD95) receptor (induces apoptosis on Fas ligand binding) antibody on DNA damage and its repair in HD-exposed HEK. Both Z-VAD-fmk and the CD95 antibody reduced HD-induced DNA degradation possibly by decreasing DNA degradation or enhancing DNA repair or both. These inhibitors may be useful for modulating DNA repair and apoptosis in HD-exposed cells with potential applications in medical management of HD-induced vesication., See also ADM001523.

Sulfur mustard (HD, bis-(2-chloroethyl) sulfide) is a vesicant that causes DNA strand breaks and apoptosis in cultured normal human epidermal keratinocytes (NHEK). HD causes apoptosis via two independent pathways, a Ca2+/calmodulin (CaM)-mediated mitochondrial pathway and Fas receptor (CD95) pathway. We studied the effects of the exogenously added CaM antagonist W7, the CD95 antibody, the caspase-3 inhibitor Ac-DEVD-CHO, and the general caspase inhibitor Z-VAD-fmk on NHEK viability loss (Calcein AM fluorescence assay, LDH release assay) due to HD. All protected against HD. Z-VAD-fmk was the most effective. These results provide a logical approach toward developing an anti-apoptotic vesicant countermeasure., See also ADM001523. Prepared in collaboration with Walter Reed Army Institute of Research, Silver Spring, MD. and Georgetown University, Washington, D.C.

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The disastrous slope failure occurred at the Kethikal hill, in the outskirts of Mangalore city in NH 13, India, during the month of June 1998 soon after the heavy and continuous monsoon rains. Many closely built dwelling houses at the top of hill are damaged and the traffic along the road is also diverted for some duration. Typical stratified slope with three layers is considered for the stability analysis and the general computer program is developed in C language for optimization of factor of safety. The factor of safety is calculated using Janbu’s generalized procedure of slices and Davidon-Fletcher-Powel (DFP) technique for optimization. The road is acting as a berm in Kethikal slope. The program gives factor of safety and the co ordinates of critical slip surface. The program is modified to take the effect of tension crack and the effect of berm. The automated stability analysis program gave least value of factor of safety in base failure with tension crack and a berm. The obtained result matches with the field observation. Kethikal landslide is due to the development of high pore pressure in soil.