Κ‐CN gene polymorphism in Nili‐ravi buffalo,Achai and Sahiwal cattle of Pakistan

Kappa‐casein (κ‐CN) is the subtype of casein protein, an important constituent of bovine milk protein. The current study was undertaken to investigate the genetic polymorphism in κ‐CN gene of Nili‐ravi buffalo, Achai and Sahiwal cattle of Pakistan using polymerase chain reaction–restriction fragment length polymorphism (PCR‐RFLP) technique. The Nili‐ravi buffalo was found to be monomorphic (genotype BB only) for κ‐CN gene. Achai cattle were polymorphic for κ‐CN (having three genotypes AA, AB and BB) with a frequency of 0.70, 0.18 and 0.12, respectively, while in Sahiwal cattle, both the genotypes AA and AB were found with genotypic frequencies of 0.92 and 0.08, respectively. The presence of genotype BB in Achai cattle is surprising as it is absent in most of the cattle breeds worldwide.     

Liaison of phenolic acids and biological activity of escalating cultivars of Daucus carota

The objective of the present study was to analyze and compare the phenolic compounds and their antioxidant capacities of new lines of Dacus carota. The selected cultivars showed high variation in the contents of total phenolics (30.26–65.39 mg/100 g FW) and total ascorbic acid (41.12–58.36 mg/100 g FW). Analysis on RP-HPLC revealed that hydroxycinnamic acids and its derivatives were major phenolic compounds present in D. carota extracts, whereas 5-caffeolquinic acid was a major hydroxycinnamic acid (ranged from 30.26 to 65.39 mg/100 g FW). DCP cultivar showed high total antioxidant capacity (77.69 mg/100 g), 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity (52.36 mg/100 g), superoxide radical scavenging capacity (53.69 mg/100 g), and hydroxyl radical scavenging capacity (51.91 mg/100 g). A linear relationship was found between total phenolic acid contents and antioxidant capacity. Both phenolic compounds and antioxidant capacities varied significantly (ρ < 0.05) among cultivars. DCP cultivar was found to be a rich source of phenolics and ascorbic acid with high antioxidant activity.     

Short communication: Molecular characteristics,antimicrobial susceptibility,and pathogenicity of clinical Nocardia cyriacigeorgica isolates from an outbreak of bovine mastitis

The occurrence of nocardial mastitis, mostly in the context of outbreaks, has been reported in many countries. However, there is a paucity of reports regarding detailed characterization of Nocardia cyriacigeorgica from bovine mastitis. Thus, herein we report characteristics, antimicrobial susceptibility patterns, molecular identification, and pathogenicity of N. cyriacigeorgica isolated from an outbreak of clinical mastitis in a dairy herd in northern China. A total of 182 (80.2%) lactating cows had clinical mastitis with severe inflammation and firmness of the udder, reduced milk production, and anorexia, with no apparent clinical response to common antibiotics. Out of 22 mastitic milk samples submitted to our laboratory, 12 N. cyriacigeorgica were isolated and characterized using standard microbiological analysis, 16S rRNA gene sequencing, random amplified polymorphic DNA PCR analysis, biochemical assays, and antibiotic susceptibility testing. Additionally, in vivo experiments were done to determine pathogenicity of these clinical mastitis isolates. All isolates were resistant to ampicillin, amoxicillin-clavulanic acid, ciprofloxacin, minocycline, rifampicin, and aminoglycosides (type VI pattern). Additionally, intramammary inoculation of mice with N. cyriacigeorgica caused chronic inflammatory changes, including hyperemia, edema, and infiltration of lymphocytes and neutrophils, as well as hyperplasia of lymph nodules in mammary glands. Therefore, we concluded that N. cyriacigeorgica was involved in the current outbreak of mastitis. To our best knowledge, this is the first report to characterize N. cyriacigeorgica isolated from cases of bovine mastitis in China.     

An interpretability security framework for intelligent decision support systems based on saliency map

International Journal of Information Security - Benefiting from the high-speed transmission and super-low latency, the Fifth Generation (5G) networks are playing an important role in contemporary...     

Observer-based leader-following consensus of one-sided Lipschitz multi-agent systems over input saturation and directed graphs

This paper investigates a local observer-based leader-following consensus control of one-sided Lipschitz (OSL) multi-agent systems (MASs) under input saturation. The proposed consensus control scheme has been formulated by using the OSL property, input saturation, directed graphs, estimated states, and quadratic inner-boundedness condition by attaining the regional stability. It is assumed that the graph always includes a (directed) spanning tree with respect to the leader root to develop matrix inequalities for investigating parameters of the proposed observer and consensus protocols. Further, a new observer-based consensus tracking method for MASs with saturation, concerning independent topologies for communicating outputs and estimates over the network, is explored to deal with a more perplexing and realistic situation. In contrast to the traditional methods, the proposed consensus approach considers output feedback and deals with the input saturation for a generalized class of nonlinear systems. The efficiency of the obtained results is illustrated via application to a group of five moving agents in the Cartesian coordinates.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

MTPSO algorithm for solving planar graph coloring problem

In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in Cui et al. (2008), not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30.     

Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor (RkNN) queries by assigning each object and query with a safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client–server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis for our Euclidean space RkNN algorithm. We show that our techniques can also be applied to answer bichromatic RkNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.