Developing neural network applications using LabVIEW

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (July 14, 2006).Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.Artificial Neural Networks (ANN) have gained tremendous popularity over the last few decades. They are considered as substitutes for classical techniques which have been followed for many years. Many neural network architectures and training algorithms have been developed so far. Different aspects of ANN such as efficiency, speed, accuracy, dependability and the like have been studied extensively. Many approaches have been suggested to improve the performance of neural nets. In this thesis, a new approach has been proposed to build neural net architectures. LabVIEW is graphical programming software developed by National Instruments. Using LabVIEW, an Application Development Environment (ADE), ready-made Virtual Instruments (VI) can be developed for various applications. This thesis concentrates on a LabVIEW approach to build various neural net structures. The learning algorithms used to train these neural nets also vary according to the requirements and application. Multi-layer feed-forward NN, Radial Basis Function NN, Principal Component NN, and Self- Organizing feature maps have been used as tools to develop applications such as pattern classification, image compression and plant modeling in a LabVIEW environment

Improved SNARK Frontend for Highly Repetitive Computations

Modern SNARK designs typically feature a frontend-backend paradigm: The frontend compiles a user\u27s program into some equivalent circuit representation, while the backend calls for a SNARK specifically made for proving satisfiability of the circuit. While the circuit may be defined over small fields, the backend prover often needs to lift the computation to much larger fields for achieving soundness. This gap results in concrete overheads, for example, when representing a SHA-256 program as a circuit with pairing-based backend SNARKs. For a class of computations that are $\textit{highly repetitive}$, we propose an improved frontend that partially bridges this gap. Compared with existing works, our frontend yields circuit representations defined over larger fields but of smaller size. Our implementation shows that for SIMD computation with $\approx 180$ SHA-256 instances, our improved frontend improves prover runtime by over $2.6 \times$ and reduces memory usage by over $1.3 \times$. Central to our result and of independent interest, is an efficient technique for proving non-native modulo arithmetic

SIEGE: Smoking Induced Epithelial Gene Expression Database

The SIEGE (Smoking Induced Epithelial Gene Expression) database is a clinical resource for compiling and analyzing gene expression data from epithelial cells of the human intra-thoracic airway. This database supports a translational research study whose goal is to profile the changes in airway gene expression that are induced by cigarette smoke. RNA is isolated from airway epithelium obtained at bronchoscopy from current-, former- and never-smoker subjects, and hybridized to Affymetrix HG-U133A Genechips, which measure the level of expression of ∼22 500 human transcripts. The microarray data generated along with relevant patient information is uploaded to SIEGE by study administrators using the database's web interface, found at http://pulm.bumc.bu.edu/siegeDB. PERL-coded scripts integrated with SIEGE perform various quality control functions including the processing, filtering and formatting of stored data. The R statistical package is used to import database expression values and execute a number of statistical analyses including t-tests, correlation coefficients and hierarchical clustering. Values from all statistical analyses can be queried through CGI-based tools and web forms found on the ‘Search’ section of the database website. Query results are embedded with graphical capabilities as well as with links to other databases containing valuable gene resources, including Entrez Gene, GO, Biocarta, GeneCards, dbSNP and the NCBI Map Viewer

SIEGE: Smoking Induced Epithelial Gene Expression Database

The SIEGE (Smoking Induced Epithelial Gene Expression) database is a clinical resource for compiling and analyzing gene expression data from epithelial cells of the human intra-thoracic airway. This database supports a translational research study whose goal is to profile the changes in airway gene expression that are induced by cigarette smoke. RNA is isolated from airway epithelium obtained at bronchoscopy from current-, former- and never-smoker subjects, and hybridized to Affymetrix HG-U133A Genechips, which measure the level of expression of ∼22 500 human transcripts. The microarray data generated along with relevant patient information is uploaded to SIEGE by study administrators using the database's web interface, found at http://pulm.bumc.bu.edu/siegeDB. PERL-coded scripts integrated with SIEGE perform various quality control functions including the processing, filtering and formatting of stored data. The R statistical package is used to import database expression values and execute a number of statistical analyses including t-tests, correlation coefficients and hierarchical clustering. Values from all statistical analyses can be queried through CGI-based tools and web forms found on the ‘Search’ section of the database website. Query results are embedded with graphical capabilities as well as with links to other databases containing valuable gene resources, including Entrez Gene, GO, Biocarta, GeneCards, dbSNP and the NCBI Map Viewer

Dew: Transparent Constant-sized zkSNARKs

We construct polynomial commitment schemes with constant sized evaluation proofs and logarithmic verification time in the transparent setting. To the best of our knowledge, this is the first result achieving this combination of properties. Our starting point is a transparent inner product commitment scheme with constant-sized proofs and linear verification. We build on this to construct a polynomial commitment scheme with constant size evaluation proofs and logarithmic (in the degree of the polynomial) verification time. Our constructions make use of groups of unknown order instantiated by class groups. We prove security of our construction in the Generic Group Model (GGM). Using our polynomial commitment scheme to compile an information-theoretic proof system yields Dew -- a transparent and constant-sized zkSNARK (Zero-knowledge Succinct Non-interactive ARguments of Knowledge) with logarithmic verification. Finally, we show how to recover the result of DARK (Bünz et al., Eurocrypt 2020). DARK presented a succinct transparent polynomial commitment scheme with logarithmic proof size and verification. However, it was recently discovered to have a gap in its security proof (Block et al, CRYPTO 2021). We recover its extractability based on our polynomial commitment construction, thus obtaining a transparent polynomial commitment scheme with logarithmic proof size and verification under the same assumptions as DARK, but with a prover time that is quadratic

Functional role of piRNAs in animal models and its prospects in aquaculture

The recent advances in the field of aquaculture over the last decade has helped the cultured-fish industry production sector to identify problems and choose the best approaches to achieve high-volume production. Understanding the emerging roles of non-coding RNA (ncRNA) in the regulation of fish physiology and health will assist in gaining knowledge on the possible applications of ncRNAs for the advancement of aquaculture. There is information available on the practical considerations of epigenetic mechanisms like DNA methylation, histone modification and ncRNAs, such as microRNA in aquaculture, for both fish and shellfish. Among the non-coding RNAs, PIWI-interacting RNA (piRNA) is 24–31 bp long transcripts, which is primarily involved in silencing the germline transposons. Besides, the burgeoning reports and studies establish piRNAs' role in various aspects of biology. Till date, there are no reviews that summarize the recent findings available on piRNAs in animal models, especially on piRNAs biogenesis and biological action. To gain a better understanding and get an overview on the process of piRNA genesis among the different animals, this work reviews the literature available on the processes of piRNA biogenesis in animal models with special reference to aquatic animal model zebrafish. This review also presents a short discussion and prospects of piRNA’s application in relevance to the aquaculture industry

Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model

Large language models (LLMs) have made significant advancements in natural language processing (NLP). Broad corpora capture diverse patterns but can introduce irrelevance, while focused corpora enhance reliability by reducing misleading information. Training LLMs on focused corpora poses computational challenges. An alternative approach is to use a retrieval-augmentation (RetA) method tested in a specific domain. To evaluate LLM performance, OpenAI's GPT-3, GPT-4, Bing's Prometheus, and a custom RetA model were compared using 19 questions on diffuse large B-cell lymphoma (DLBCL) disease. Eight independent reviewers assessed responses based on accuracy, relevance, and readability (rated 1-3). The RetA model performed best in accuracy (12/19 3-point scores, total=47) and relevance (13/19, 50), followed by GPT-4 (8/19, 43; 11/19, 49). GPT-4 received the highest readability scores (17/19, 55), followed by GPT-3 (15/19, 53) and the RetA model (11/19, 47). Prometheus underperformed in accuracy (34), relevance (32), and readability (38). Both GPT-3.5 and GPT-4 had more hallucinations in all 19 responses compared to the RetA model and Prometheus. Hallucinations were mostly associated with non-existent references or fabricated efficacy data. These findings suggest that RetA models, supplemented with domain-specific corpora, may outperform general-purpose LLMs in accuracy and relevance within specific domains. However, this evaluation was limited to specific questions and metrics and may not capture challenges in semantic search and other NLP tasks. Further research will explore different LLM architectures, RetA methodologies, and evaluation methods to assess strengths and limitations more comprehensively

Smoking-induced gene expression changes in the bronchial airway are reflected in nasal and buccal epithelium

<p>Abstract</p> <p>Background</p> <p>Cigarette smoking is a leading cause of preventable death and a significant cause of lung cancer and chronic obstructive pulmonary disease. Prior studies have demonstrated that smoking creates a field of molecular injury throughout the airway epithelium exposed to cigarette smoke. We have previously characterized gene expression in the bronchial epithelium of never smokers and identified the gene expression changes that occur in the mainstem bronchus in response to smoking. In this study, we explored relationships in whole-genome gene expression between extrathorcic (buccal and nasal) and intrathoracic (bronchial) epithelium in healthy current and never smokers.</p> <p>Results</p> <p>Using genes that have been previously defined as being expressed in the bronchial airway of never smokers (the "normal airway transcriptome"), we found that bronchial and nasal epithelium from non-smokers were most similar in gene expression when compared to other epithelial and nonepithelial tissues, with several antioxidant, detoxification, and structural genes being highly expressed in both the bronchus and nose. Principle component analysis of previously defined smoking-induced genes from the bronchus suggested that smoking had a similar effect on gene expression in nasal epithelium. Gene set enrichment analysis demonstrated that this set of genes was also highly enriched among the genes most altered by smoking in both nasal and buccal epithelial samples. The expression of several detoxification genes was commonly altered by smoking in all three respiratory epithelial tissues, suggesting a common airway-wide response to tobacco exposure.</p> <p>Conclusion</p> <p>Our findings support a relationship between gene expression in extra- and intrathoracic airway epithelial cells and extend the concept of a smoking-induced field of injury to epithelial cells that line the mouth and nose. This relationship could potentially be utilized to develop a non-invasive biomarker for tobacco exposure as well as a non-invasive screening or diagnostic tool providing information about individual susceptibility to smoking-induced lung diseases.</p

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