Structural modeling and analysis of non-mammalian CA VI

Pentraxins are a family of host defense components of the innate immune system and phylogenetically conserved pattern recognition proteins. The short pentraxins are serum amyloid P component (SAP) and C-reactive protein (CRP), and the long pentraxins are pentraxin 3 (PTX3), which are only pattern recognization molecules. In non-mammalian species, carbonic anhydrase VI (CA VI) contains an additional short pentraxin domain, and short pentraxins form pentamer units. Our study is an effort to understand the interaction properties pattern of the pentameric model of zebrafish CA VI+PTX complex. Our results based on the comparisons between SAP and CRP structures and the cluster between their protomers seem different as the list of contacts varies. Between the pentameric model of zebrafish CA VI+PTX complex and CRP electrostatic surfaces, significant hydrophilic and hydrophobic differences are observed among the CA VI+PTX complex and CRP. The counted conserved residues in both pentraxins are the same, but the fact that conserved residues in similar positions and conserved between SAP and CRP. The SAP has a lot more conservation, which is buried, and it seems to have more contact structure because many of these essential residues are buried, at least 95%. The interface residues are not highly conserved but whereas the calcium-binding sites are relatively conserved. The Ca2+ binding sites of pentraxins are organized in a similar mode, and Ca2+ ions coordinating residues are the same in both pentraxins. Finally, compare the contact between SAP and CRP. The amount of contact or amount of contact residue is similar, only that the mode of binding is very different. Because of the difference and low conservation of contacting residues, it is concluded that the mode of Ca6 associated PTX cannot be fully predicted based on this study. However, multiple van der Waals contacts and one ionic contact were observed to be conserved between CRP and SAP, which may form a basis for understanding contacts between CA VI +PTX monomers

A Study on the Default Supplemental Adjustment Factors of Progression Adjustment Factor Formula under Non-Lane Based Traffic Condition

A mathematical expression of supplemental adjustment factor (fPA) has provided a way to estimate Progression Adjustment Factor (P.F.) more accurately as the P.F. relies on the fPA value. The mathematical formula of fPA requires traffic parameters that need to be determined from the field survey. As non-lane based traffic behavior is significantly different from lane-based traffic, this study examines the applicability of the default fPA values of Highway Capacity Manual-2000 under the non-lane based traffic condition. Default values for all six Arrival Types (AT1 to AT6) are reviewed against an available mathematical expression. After performing statistical analysis on the collected data, it is found that the HCM-provided default fPA values for AT1, AT3, AT5 and AT6 are consistent with the mathematical expression. However, the supplemental factors for AT2 and AT4 are found to vary significantly from the default values. By considering the mathematical expression as a standard of comparison, a graphical representation of error corresponding to trial fPA value shows that a value of 0.99 for fPA provides the minimum error of P.F. for AT2. Thus, the default value (fPA =0.93) is found to underestimate fPA as well as P.F. by 6.5%. In a similar way, the fPA value for AT4 is found to be 0.96 which is 16.5% less than the default value. So, the default value (fPA =1.15) overestimates fPA and P.F. by 16.5%. Therefore, fPA = 0.99 for AT2 and fPA = 0.96 for AT4 should be used to estimate P.F. in case of non-lane based traffic

Surface Modification of Wool Fabric with Chitosan and Gamma Radiation

Abstract Surface modification of wool was done with chitosan, gamma radiation and combined chitosan/gamma radiation to analyze how polymer loading%, k/s value, dye uptake% and wash fastness property vary of the dyed fabric. In this investigation scoured and bleached knitted wool fabric was treated with 0.1%, 0.3%, 0.5%, 0.7% and 1% chitosan and polymer loading was found as of 1.21%, 1.38%, 0.32%, 0.14% and 0.48% respectively. It was found that polymer loading % increase significantly with the decrease of chitosan concentration % up to a certain limit. Some samples were irradiated with 5kGy, 10kGy, 20kGy and 50kGy gamma radiation individually. Combined chitosan/gamma treatment was carried out by treating with 0.1% chitosan followed by treatment with 5kGy, 10kGy, 20kGy and 50kGy gamma radiation. The infrared spectrum of wool specimens were analyzed by Fourier Transform Infrared Spectrometer (FT-IR). It was found that the infrared spectrum of untreated and treated wool specimens were approximately same except the peak absorbs at 1340-1265 cm -1 which indicate the C-N stretch absorption of aromatic amines. After dyeing k/s value of untreated and treated wool specimens were measured using Data color 600 ® . There was a remarkable variation of k/s value on different treatment process. Dye uptake% of untreated and treated wool specimens were determined by using UV Visible Spectrophotometer in terms of absorbency. Color fastness to wash was measured using ISO standard. There was no significant change of wash fastness property of treated and untreated wool

AI-Driven Cybersecurity: Balancing Advancements and Safeguards

As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%