7,070 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    Computational Stylistics in Poetry, Prose, and Drama

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    The contributions in this edited volume approach poetry, narrative, and drama from the perspective of Computational Stylistics. They exemplify methods of computational textual analysis and explore the possibility of computational generation of literary texts. The volume presents a range of computational and Natural Language Processing applications to literary studies, such as motif detection, network analysis, machine learning, and deep learning

    Search-Based Regular Expression Inference on a GPU

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    Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. Collectively, the bitvectors represent, as characteristic sequences, all sub-languages of the infix-closure of the union of positive and negative examples. Mathematically, this is a semiring of (a variant of) formal power series. Infix-closure enables bottom-up compositional construction of larger from smaller regular expressions using the operations of our semiring. This minimises data movement and data-dependent branching, hence maximises data-parallelism. In addition, the infix-closure remains unchanged during the search, hence search can be staged: first pre-compute various expensive operations, and then run the compute intensive search process. We provide two C++ implementations, one for general purpose CPUs and one for Nvidia GPUs (using CUDA). We benchmark both on Google Colab Pro: the GPU implementation is on average over 1000x faster than the CPU implementation on the hardest benchmarks

    Research on Pathogenic Fungi and Mycotoxins in China

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    Researchers have made great progress in the mechanism of mycotoxins biosynthesis, pathogenicity, and the construction of detection methods. This collection holds fundamental and applied research on pathogenic fungi and mycotoxins in China, which directly or indirectly affect the health of plants, animals, and humans. Some papers mainly study the mechanism of mycotoxin biosynthesis, including the crucial genes, environmental factors, and regulation mechanisms, which is beneficial to advancing our knowledge of this area. Some papers focus on the toxicity of mycotoxins, which is helpful to the detoxification of mycotoxins and the reduction of the hazardous effect of mycotoxins. Special attention is given to the leading detection methods, especially on-site rapid detection. These effective and feasible methods are important for discovering and controlling the risk of mycotoxins

    The Complexities of Long Distance: a relationship between the gut microbiota and breast cancer

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    Antibiotic induced perturbations of the gut microbiota are increasingly associated with the progression of several cancers. In contrast, the promotion of a healthy microbiota, often through probiotic supplementation, can improve treatment outcomes and bolster anti-cancer immune responses in tumours. However, studies involving breast cancer specifically are relatively limited. In 2020, breast cancer was the most frequently diagnosed cancer globally and carried one of the highest mortality rates among all cancer types. Additionally, the use of antibiotics in breast cancer treatment pathways is common. Thus, the research presented in this thesis aims to expand the current understanding of the influences the microbiota has on breast cancer, particularly regarding the downstream effects of antibiotic administration. The results demonstrate that antibiotic-induced perturbations of the microbiota promote primary breast cancer tumour progression in at least two different orthotopic models of the disease. Antibiotics did not influence tumour progression in germ-free animals, confirming that this progression was microbiota dependent. Single cell transcriptomics and histological analysis identified mast cells as being increased in tumours from antibiotic treated animals and, subsequently, inhibiting mast cell function rescued tumour growth in antibiotic treated animals to sizes similar to those observed in a control group at the same timepoint. Shotgun metagenomic sequencing of caecal samples identified reductions in several bacterial species in antibiotic treated animals. One such species, Faecalibaculum rodentium, has known anti-tumourigenic influence in colorectal cancer models and, when supplemented to antibiotic treated animals, rescued tumour growth. Efforts were made to investigate how perturbation of the microbiota influenced breast cancer metastasis, but the results obtained suggest further research is required to fully understand such influences. The implications of this research suggest that the use of antibiotics in breast cancer treatment pathways should be cautious and clinical guidance regarding their administration may need to be revised

    Quantifying Dissolved Lignin Phenols in the Albemarle–Pamlico Estuarine System and Coastal North Carolina following the 2018 Hurricane Florence

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    The global carbon cycle (GCC) involves the exchange and transformation of carbon across the ocean, atmosphere, and terrestrial reservoirs. It consists of a positive feedback loop involving dissolved organic matter in natural waters, greenhouse gases, and global warming. Extreme weather events such as hurricanes can accelerate this feedback loop by rapidly increasing the flux of organic matter to coastal systems. The 2018 Atlantic Ocean hurricane season with its numerous and large storms, adversely impacted North Carolina, USA. One of the largest storms of the season was Hurricane Florence, which released over 900 mm of precipitation across the state causing runoff, erosion, and flooding of terrestrial organic material (TOM). This study quantified dissolved lignin, a bulky biopolymer that is found within the cell wall of vascular land plants, as a biomarker of TOM influx into the Albemarle–Pamlico Estuarine System (APES) and coastal ocean of North Carolina. Water samples were collected in October and November of 2018 which represented hurricane–influenced samples. In contrast, water samples collected in July 2019 represented non–storm conditions. All water samples were filtered at 0.2 µm to isolate and analyze the truly dissolved phase. A subset of each filtered water sample were sent to the NC State Stable Isotope Laboratory for bulk dissolved organic carbon (d13CDOC) analysis, to help identify its source. Additionally, an existing method for quantifying dissolved lignin, originally designed for liquid chromatography, was modified to allow low volume quantification of dissolved lignin via gas chromatography–mass spectrometry (GC–MS). Dissolved lignin was quantified in 3 replicates each of standard reference materials (SRM): NIST SRM 1944 (New York/ New Jersey Waterway Sediment), NIST SRM 8704 (Buffalo River Sediment), and Aldrich Humic Acid. Total dissolved lignin was determined to be (160.12 ± 12.84 �g L–1), (129.87 ± 6.30 �g L–1) , and (1,124.34 ± 33.21 �g L–1), in SRM 1944, 8704, and Aldrich Humic Acid, respectively. There are no existing measurements of dissolved lignin in these reference materials in the literature. Thus, the low standard deviation and high precision across replicates suggested that the method that was developed was precise, and thus applicable to natural water samples. Water samples collected from the APES and coastal ocean system in 2018 yielded overall higher concentrations of total dissolved lignin (2.84 ± 1.84 �g L–1) than those collected during 2019 (0.67 ± 0.67 �g L–1). The estuary had, on average, higher dissolved lignin concentrations in both 2018 and 2019 than that of the coastal ocean waters. Lignin interclass ratios of syringyl to vanillyl monomers (S/V; angiosperm vs gymnosperm) and C/V (cinnamyl to vanillyl; grasses versus woods) were calculated and used to provide insight on TOM sources. Average S/V values were 1.96 ± 2.71 and 2.10 ± 2.86, in 2018 and 2019 respectively. Average C/V were 1.54 ± 1.80 and 2.33 ± 2.94, in 2018 and 2019 respectively. Degradation of TOM was determined using acid to aldehyde ratio of vanillyl and syringyl monomers; [Ad/Al]V and [Ad/Al]S. Ratios of [Ad/Al]V were 8.11 ± 15.93 and 3.69 ± 4.58, in 2018 and 2019 respectively. Similarly, ratios of [Ad/Al]S were 11.11 ± 26.18 and 8.26 ± 10.98, in 2018 and 2019 respectively. These ratios in conjunction with d13CDOC which was –27.9 ± 1.7 and –26.6 ± 0.9 % for 2018 and 2019 respectively, suggested that the dissolved lignin samples of 2018 were generally derived from fresh angiosperm and gymnosperm tissues, while 2019 samples were much more degraded in comparison but had similar plant tissue sources. The overarching results from this study indicate that Hurricane Florence directly increased the concentration of terrestrially derived dissolved lignin to the APES and coastal North Carolina by approximately 76.4 % via erosion, flooding, and runoff. Such rapid pulses of TOM have been shown in other coastal ecosystems to increase regional concentrations of CO2(g), a greenhouse gas. Although not directly quantified in this study, a similar effect likely occurred in coastal NC because of the 2018 hurricane season
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