Co-Infection of HSV in Gonococcal Urethritis Patients

Co-infection with two different pathogens may alter the classical clinical course that manifests infection as single pathogen. In STIs, such co-infection may trigger the reactivation of a latent infection, and syndromic approach may not be insufficient to free the host of the entire gamut of infectivity agents. Present study analyzed appropriate samples for Neisseria gonorrheae and HSV from 200 patients presented to STI clinic. Gonorrhea was detected in 4% and HSV in 5% of patients. 25% of gonorrhea patients had HSV-2 co-infection with an overall 4.5% yield of subclinical HSV cases which would have been missed leading to inappropriate treatment, risk of recurrence and transmission to contacts. Awareness regarding encounter with multiple infections is necessary for effective management

Building a Llama2-finetuned LLM for Odia Language Utilizing Domain Knowledge Instruction Set

Building LLMs for languages other than English is in great demand due to the unavailability and performance of multilingual LLMs, such as understanding the local context. The problem is critical for low-resource languages due to the need for instruction sets. In a multilingual country like India, there is a need for LLMs supporting Indic languages to provide generative AI and LLM-based technologies and services to its citizens. This paper presents our approach of i) generating a large Odia instruction set, including domain knowledge data suitable for LLM fine-tuning, and ii) building a Llama2-finetuned model tailored for enhanced performance in the Odia domain. The proposed work will help researchers build an instruction set and LLM, particularly for Indic languages. We will release the model and instruction set for the public for research and noncommercial purposes

HALT Evaluation of SJ BIST Technology for Electronic Prognostics

Abstract-This At the end of the three-month HALT, selected FPGAs were subjected to die-and-pry and cross-section examination and comparison to the collected data. Analysis confirmed that SJ BIST did report the occurrence of faults on damaged pins, and SJ BIST did not report any false negatives. The HALT confirmed the efficacy, accuracy, and reliability of SJ BIST as both a prognostic and diagnostic tool for FPGAs in BGA type of packages

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

On Dimensionality of Coordinate-Based Network Distance Mapping

In this paper, we investigate the veracity of a basic premise, “that network distance is Euclidean”, assumed in a class of recently proposed techniques that embed Internet hosts in a Euclidean space for the purpose of estimating the delay or “distance” between them. Using the classical scaling method on a number of network distance measurement datasets, we observe “non-Euclidean-ness” in the network distance. We find that this “non-Euclideanness” is caused by the clustering effect of Internet hosts. We also observe that the distance between the nodes in the same cluster is significantly more non-Euclidean than the distance between nodes in different clusters. Our correlation dimension based analysis of intrinsic dimensionality of the datasets reveals that the network distances seem to have a fractional dimension between 2 and 3. We observe that further increasing the dimensionality does not improve the accuracy of the embedding in Euclidean space. Motivated by these results, we propose a new hybrid model for embedding the network nodes using only a 2-dimensional Euclidean coordinate system and small adjustment terms. We show that the accuracy of the proposed embedding technique is as good as, if not better than, that of a 7-dimensional Euclidean embedding.Lee, Sanghwan; Zhang, Zhi-Li; Sahu, Sambit; Saha, Debanjan. (2005). On Dimensionality of Coordinate-Based Network Distance Mapping. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215663

On suitability of Euclidean embedding of Internet hosts

In this paper, we investigate the suitability of embedding Internet hosts into a Euclidean space given their pairwise distances (as measured by round-trip time). Using the classical scaling and matrix perturbation theories, we first establish the (sum of the) magnitude of negative eigenvalues of the (doubly-centered, squared) distance matrix as a measure of suitability of Euclidean embedding. We then show that the distance matrix among Internet hosts contains negative eigenvalues of large magnitude, implying that embedding the Internet hosts in a Euclidean space would incur relatively large errors. Motivated by earlier studies, we demonstrate that the inaccuracy of Euclidean embedding is caused by a large degree of triangle inequality violation (TIV) in the Internet distances, which leads to negative eigenvalues of large magnitude. Moreover, we show that the TIVs are likely to occur locally, hence, the distances among these close-by hosts cannot be estimated accurately using a global Euclidean embedding, in addition, increasing the dimension of embedding does not reduce the embedding errors. Based on these insights, we propose a new hybrid model for embedding the network nodes using only a 2-dimensional Euclidean coordinate system and small error adjustment terms. We show that the accuracy of the proposed embedding technique is as good as, if not better, than that of a 7-dimensional Euclidean embedding