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In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e ., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

Dipeptidyl peptidase-4 (DPP-4) is a proteolytic enzyme responsible for the rapid degradation of Glucagon-like peptide 1 (GLP-1) that is required for the secretion of insulin. DPP-4 also influences activation of node like receptor family, pyrin domain containing 3 (NLRP3) inflammasome under diabetic conditions. Although several polyphenols are reported for various bioactivities, they are consumed as part of the food matrix and not in isolation. Horsegram (Macrotyloma uniflorum) is a rich source of myricetin (Myr) (35 μg/g flour), reported for its anti-hyperglycemic effect. In this investigation, we aimed to study the effect of Myr, singly, and in the presence of co-nutrient horsegram protein (HP) on DPP-4 activity and its consequential impact on GLP-1, insulin, and NLRP3 inflammasome in high-fat diet and single low dose streptozotocin (STZ)-induced diabetic male Wistar rats. In diabetic control (DC), the activity of DPP-4 and its expression were higher compared to treated groups. The consequential decrease in the circulating GLP-1 levels in the DC group, but not treated groups, further indicated the effectiveness of our test molecules under diabetic conditions. Specifically, Myr decreased DPP-4 activity and its expression levels with enhanced circulating GLP-1 and insulin levels. Myr administration also resulted in a lessening of diabetes-induced NLRP3 inflammasome activation and enhanced antioxidant enzyme activities. HP also proved to be efficient in reducing elevated blood glucose levels and enhancing antioxidant enzyme activities. However, Myr, in the presence of HP as a co-nutrient, had diminished capacity to inhibit DPP-4 and, consequently, reduced potential in ameliorating diabetic conditions. Myr proved to be a potent inhibitor of DPP-4 in vitro and in vivo, resulting in enhanced circulating GLP-1 and insulin levels, thereby improving diabetic conditions. Though Myr and HP, individually ameliorate diabetic conditions, their dietary combination had reduced efficiency.

Aspergillus oryzae MTCC 5341, when grown on wheat bran as substrate, produces several extracellular acid proteases. Production of the major acid protease (constituting 34% of the total) by solid-state fermentation is optimized. Optimum operating conditions obtained are determined as pH 5, temperature of incubation of 30°C, defatted soy flour addition of 4%, and fermentation time of 120 h, resulting in acid protease production of 8.64 × 10⁵ U/g bran. Response-surface methodology is used to generate a predictive model of the combined effects of independent variables such as, pH, temperature, defatted soy flour addition, and fermentation time. The statistical design indicates that all four independent variables have significant effects on acid protease production. Optimum factor levels are pH 5.4, incubation temperature of 31°C, 4.4% defatted soy flour addition, and fermentation time of 123 h to yield a maximum activity of 8.93 × 10⁵ U/g bran. Evaluation experiments, carried out to verify the predictions, reveal that A. oryzae produces 8.47 × 10⁵ U/g bran, which corresponds to 94.8% of the predicted value. This is the highest acid protease activity reported so far, wherein the fungus produces four times higher activity than previously reported [J Bacteriol 130(1): 48-56, 1977].

Microbial milk-clotting enzymes are valued as calf rennet substitutes in the cheese industry. Aspergillus oryzae MTCC 5341 was identified to produce the highest milk-clotting activity during screening of 16 fungal strains. Solid state fermentation using wheat bran along with 4% defatted soy flour and 2% skim milk powder as substrate was optimal for growth of A. oryzae and production of the enzyme. Nearly 40,000 U/g bran of milk-clotting activity was present at the end of 120 h. The enzyme could be recovered by percolating the bran with 0.1 M sodium chloride for 60 min at 4°C. The decolorized enzyme preparation had high ratio of milk clotting to proteolytic activity. Affinity precipitation with alginate and subsequent elution with 0.5 M sodium chloride containing 0.2 M CaCl₂ resulted in an enzyme preparation with specific activity of 3,500 U/mg and 72% yield. Optimum pH and temperature for activity of the enzyme were characterized as 6.3 and 55°C, respectively. Milk-clotting enzyme showed differential degree of hydrolysis on casein components. High ratio of milk clotting to proteolytic activity coupled with low thermal stability strengthens the potential usefulness of milk-clotting enzyme of A. oryzae MTCC 5341 as a substitute for calf rennet in cheese manufacturing.

The endotoxin lipopolysaccharide (LPS) promotes sepsis, but bacterial peptides also promote inflammation leading to sepsis. We found, intraperitoneal administration of live or heat inactivated E. coli JE5505 lacking the abundant outer membrane protein, Braun lipoprotein (BLP), was less toxic than E. coli DH5α possessing BLP in Swiss albino mice. Injection of BLP free of LPS purified from E. coli DH5α induced massive infiltration of leukocytes in lungs and liver. BLP activated human polymorphonuclear cells (PMNs) ex vivo to adhere to denatured collagen in serum and polymyxin B independent fashion, a property distinct from LPS. Both LPS and BLP stimulated the synthesis of platelet activating factor (PAF), a potent lipid mediator, in human PMNs. In mouse macrophage cell line, RAW264.7, while both BLP and LPS similarly upregulated TNF-α and IL-1β mRNA; BLP was more potent in inducing cyclooxygenase-2 (COX-2) mRNA and protein expression. Peritoneal macrophages from TLR2 −/− mice significantly reduced the production of TNF-α in response to BLP in contrast to macrophages from wild type mice. We conclude, BLP acting through TLR2, is a potent inducer of inflammation with a response profile both common and distinct from LPS. Hence, BLP mediated pathway may also be considered as an effective target against sepsis.

In cloud computing environment, load balancing is an important task. So many of the researchers had focused on load balanced scheduling technique. Those provides better load balancing in cloud but there are some issues like resource allocation and cost maintenance. One of the major issue in load balancing techniques is service level agreement (SLA) management because many of them are affected by this SLA-violation. Many researchers have proposed various risk based framework but few of them has guides the service provider to take steps for SLA violation abatement and they also need some improvements. To tackle this problem, a new SLA-aware risk management framework (SA-RMF) is proposed in this work for efficient load balancing in cloud. A new technique is presented here based on CPU parameter for generating efficient dynamic threshold. A better quality of service values prediction is achieved by using hybrid approach in SA-RMF. Through experiments, the appropriateness of proposed system is demonstrated and validated that it helps cloud providers to mitigate future violations of services and consequences.

In mobile ad hoc network (MANET), the traffic load impacts energy utilization and throughput. It can be overcome by efficient congestion control and link discontinuity identifier techniques. To improve the overall network performance of MANET, the link discontinuity and optimal route data delivery (LDORDD) protocol has been implanted based on AOMDV protocol is proposed along with cross-layer design. In this approach, local route recovery and transmission queue management (TQM) activities are used to control the packet's drop and congestion. This protocol works on buffering the incoming packets at the TQM layer, which controls the packets drop by locally repairing the route. The simulation results of the LDORDD protocol is evaluated in terms of network throughput, packet delivery ratio, and average end to end delay. The numerical and graphical representation of the LDORDD proves that the network resource rudiments are enhanced when compared to the existing methods.