Isolation and characterization of gelatin-binding proteins from goat seminal plasma

A family of proteins designated BSP-A1, BSP-A2, BSP-A3 and BSP-30 kDa (collectively called BSP proteins for Bovine Seminal Plasma proteins) constitute the major protein fraction in the bull seminal plasma. These proteins interact with choline phospholipids on the sperm surface and play a role in the membrane stabilization (decapacitation) and destabilization (capacitation) process. Homologous proteins have been isolated from boar and stallion seminal plasma. In the current study we report the isolation and preliminary characterization of homologous proteins from goat seminal plasma. Frozen semen (-80°C) was thawed and centrifuged to remove sperm. The proteins in the supernatant were precipitated by the addition of cold ethanol. The precipitates were dissolved in ammonium bicarbonate and lyophilised. The lyophilised proteins were dissolved in phosphate buffer and loaded onto a gelatin-agarose column, which was previously equilibrated with the same buffer. The column was successively washed with phosphate buffer, with phosphate buffer saline and with 0.5 M urea in phosphate buffer saline to remove unadsorbed proteins, and the adsorbed proteins were eluted with 5 M urea in phosphate buffer saline. Analysis of pooled, dialysed and lyophilised gelatin-agarose adsorbed protein fraction by SDS-PAGE indicated the presence of four protein bands that were designated GSP-14 kDa, GSP-15 kDa, GSP-20 kDa and GSP-22 kDa (GSP, Goat Seminal Plasma proteins). Heparin-affinity chromatography was then used for the separation of GSP-20 and -22 kDa from GSP-14 and -15 kDa. Finally, HPLC separation permitted further isolation of each one from the other. Amino acid sequence analysis of these proteins indicated that they are homologous to BSP proteins. In addition, these BSP homologs bind to hen's egg-yolk low-density lipoproteins. These results together with our previous data indicate that BSP family proteins are ubiquitous in mammalian seminal plasma, exist in several forms in each species and possibly play a common biological role

Big Data Analytics Using Apache Flink for Cybercrime Forensics on X (formerly known as Twitter)

The exponential growth of social media usage has led to massive data sharing, posing challenges for traditional systems in managing and analyzing such vast amounts of data. This surge in data exchange has also resulted in an increase in cyber threats from individuals and criminal groups. Traditional forensic methods, such as evidence collection and data backup, become impractical when dealing with petabytes or terabytes of data. To address this, Big Data Analytics has emerged as a powerful solution for handling and analyzing structured and unstructured data. This thesis explores the use of Apache Flink, an open-source tool by the Apache Software Foundation, to enhance cybercrime forensic research. Unlike batch processing engines like Apache Spark, Apache Flink offers real-time processing capabilities, making it well-suited for analyzing dynamic and time-sensitive data streams. The study compares Apache Flink's performance against Apache Spark in handling various workloads on a single node. The literature review reveals a growing interest in utilizing Big Data Analytics, including platforms like Apache Flink, for cybercrime detection and investigation, especially on social media platforms like X (formerly known as Twitter). Sentiment analysis is a vital technique, but challenges arise due to the unique nature of social data. X (formerly known as Twitter), as a valuable source for cybercrime forensics, enables the study of fraudulent, extremist, and other criminal activities. This research explores various data mining techniques and emphasizes the need for real-time analytics to combat cybercrime effectively. The methodology involves data collection from X, preprocessing to remove noise, and sentiment analysis to identify cybercrime-related tweets. The comparative analysis between Apache Flink and Apache Spark demonstrates Flink's efficiency in handling larger datasets and real-time processing. Parallelism and scalability are evaluated to optimize performance. The results indicate that Apache Flink outperforms Apache Spark regarding response time, making it a valuable tool for cybercrime forensics. Despite progress, challenges such as data privacy, accuracy improvement, and cross-platform analysis remain. Future research should focus on refining algorithms, enhancing scalability, and addressing these challenges to further advance cybercrime forensics using Big Data Analytics and platforms like Apache Flink

Big Data Analytics Using Apache Flink for Cybercrime Forensics on X (formerly known as Twitter)

The exponential growth of social media usage has led to massive data sharing, posing challenges for traditional systems in managing and analyzing such vast amounts of data. This surge in data exchange has also resulted in an increase in cyber threats from individuals and criminal groups. Traditional forensic methods, such as evidence collection and data backup, become impractical when dealing with petabytes or terabytes of data. To address this, Big Data Analytics has emerged as a powerful solution for handling and analyzing structured and unstructured data. This thesis explores the use of Apache Flink, an open-source tool by the Apache Software Foundation, to enhance cybercrime forensic research. Unlike batch processing engines like Apache Spark, Apache Flink offers real-time processing capabilities, making it well-suited for analyzing dynamic and time-sensitive data streams. The study compares Apache Flink's performance against Apache Spark in handling various workloads on a single node. The literature review reveals a growing interest in utilizing Big Data Analytics, including platforms like Apache Flink, for cybercrime detection and investigation, especially on social media platforms like X (formerly known as Twitter). Sentiment analysis is a vital technique, but challenges arise due to the unique nature of social data. X (formerly known as Twitter), as a valuable source for cybercrime forensics, enables the study of fraudulent, extremist, and other criminal activities. This research explores various data mining techniques and emphasizes the need for real-time analytics to combat cybercrime effectively. The methodology involves data collection from X, preprocessing to remove noise, and sentiment analysis to identify cybercrime-related tweets. The comparative analysis between Apache Flink and Apache Spark demonstrates Flink's efficiency in handling larger datasets and real-time processing. Parallelism and scalability are evaluated to optimize performance. The results indicate that Apache Flink outperforms Apache Spark regarding response time, making it a valuable tool for cybercrime forensics. Despite progress, challenges such as data privacy, accuracy improvement, and cross-platform analysis remain. Future research should focus on refining algorithms, enhancing scalability, and addressing these challenges to further advance cybercrime forensics using Big Data Analytics and platforms like Apache Flink

Molecular mechanisms of aluminum oxide thin film growth on polystyrene during atomic layer deposition

One layer at a time: A proposed growth mechanism of Al2O 3 atomic layer deposition on a polystyrene surface is presented. The infiltration of trimethylaluminum and H2O precursors may result in Al2O3 growth in the polystyrene matrix (subsurface), thus forming a hybrid interface (shown in the green box) of C8H 7O- and C6H4O2Al - ions