Tandem Mass Spectrometry (MS/MS) for Determination of Architecture of Synthetic Polymers

Tandem Mass Spectrometry (MS/MS) was used to determine the architecture of synthetic polymers and they are difluorene-N3, 3-difluorene-N3 and VPOSS-3-difluorene. VPOSS materials usually exhibit electrochemical properties and the research was performed to determine their accurate structure and to provide evidence of their proper synthesis. The compounds were mixed with a matrix solution, DCTB, and a silver salt to enable proper protonation. The solvent mixture was on a MALDI plate and then put into the mass spectrometer. The mass spectrum generated was analyzed and each fragment was identified to structures with the synthetic polymers via ChemDraw. Thus, trial and error method was used in identifying each fragment peak in the mass spectrum and the final structure of the polymers was verified

Adversarial Robustness of Learning-based Static Malware Classifiers

Malware detection has long been a stage for an ongoing arms race between malware authors and anti-virus systems. Solutions that utilize machine learning (ML) gain traction as the scale of this arms race increases. This trend, however, makes performing attacks directly on ML an attractive prospect for adversaries. We study this arms race from both perspectives in the context of MalConv, a popular convolutional neural network-based malware classifier that operates on raw bytes of files. First, we show that MalConv is vulnerable to adversarial patch attacks: appending a byte-level patch to malware files bypasses detection 94.3% of the time. Moreover, we develop a universal adversarial patch (UAP) attack where a single patch can drop the detection rate in constant time of any malware file that contains it by 80%. These patches are effective even being relatively small with respect to the original file size -- between 2%-8%. As a countermeasure, we then perform window ablation that allows us to apply de-randomized smoothing, a modern certified defense to patch attacks in vision tasks, to raw files. The resulting `smoothed-MalConv' can detect over 80% of malware that contains the universal patch and provides certified robustness up to 66%, outlining a promising step towards robust malware detection. To our knowledge, we are the first to apply universal adversarial patch attack and certified defense using ablations on byte level in the malware field

Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal

In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in Biology and Medicin

Bacteriophage and non-pathogenic Vibrio to control diseases in shrimp aquaculture

The study aimed to address the recurring outbreaks of microbial diseases in shrimp aquaculture in Bangladesh the study focused on the utilization of bacteriophages and non-pathogenic Vibrio. The bacteriophages were isolated from sewage water sample collected from shrimp farm, hatchery, and the JUST campus. The bacteriophages were tested for their ability to infect different Vibrio strains in order to assess their bacteriolytic activity. Non-pathogenic Vibrio strains were obtained from suspected diseased isolates collected from the south-western region of Bangladesh through PCR amplification. In laboratory tests, the bacteriophages successfully infected 91 % of the tested Vibrio strains (19 out of 21 strains). In the experimental unit, shrimp treated with phage prophylaxis and phage treatment demonstrated notable protection against AHPND and was able to survive a deadly bacterial challenge. A total of 35 suspected diseased isolates were tested, and PCR amplification revealed 6 non-pathogenic Vibrio strains. In field trials, cultured bacteriophages were applied at a concentration of 1.5×106 PFU/ml, while non-pathogenic Vibrio was applied at 5×105 CFU/ml. The trials showed increased protection against infections and no severe deaths during the adaptive research phase. The cultured shrimp were analyzed morphologically and showed a muscle gut ratio greater than 4:1. No abnormal deformities were observed in their appendages or overall body, suggesting their overall health and well-being. The bacteriological tests conducted on the shrimp samples (application of bacteriophages and non-pathogenic Vibrio) revealed that 18 % of them were infected with bacteria, primarily Vibrio cholerae, Vibrio parahaemolyticus, and other bacterial species. Despite this, the infections did not lead to a disease outbreak; PCR amplification showed negative results for AHPND, White Spot Syndrome Virus (WSSV), and Enterocytozoon hepatopenaei (EHP). The results highlight the potential of using bacteriophages and non-pathogenic Vibrio as a sustainable solution for preventing and controlling microbial diseases in shrimp aquaculture. Furthermore, this study will contribute valuable insights into the development of alternative strategies to combat antibiotic resistance and promote the growth of the shrimp industry in Bangladesh