Lessons from Bilski

In this paper, I will examine how the U.S. and Canadian courts have approached the patentability of intangible inventions and discuss whether any lessons can be learned from the U.S.’s patent dilemma. In section 2, I will review the American jurisprudence on patentability of intangible inventions. In section 3, I will discuss the potential impact Bilski may have on the American jurisprudence. Section 4 will assess the Canadian jurisprudence on patentability of intangible inventions. In section 5, I will discuss the Federal Court of Canada’s decision in Amazon/FCC. I argue that based on recent events in the American jurisprudence, Canadian courts should carefully consider the consequences of opening up patent protection to intangible inventions because granting too much patent protection can impede innovation and endanger the patent system

Molecular mechanisms of mouse embryonic stem cell differentiation

Mouse embryonic stem (ES) cells are pluripotent cells, meaning that they can give rise to all tissues in the body. This has catalyzed research in both early embryogenesis as a model system for mammalian development as well as regenerative medicine as a renewable source of unspecialized cells which can be converted into nearly any cell type required by a patient. ES cells have been an invaluable resource for advancing fundamental understanding of global transcriptional and epigenetic regulations, signaling pathways, and noncoding RNA in mammalian systems. However, the molecular mechanisms of how ES cells are differentiated remain much less understood. Differentiation is a complex process involving actions of ES cell core factors, lineage specific regulators, epigenetic modifications, and chromatin remodelers. Thus, a single reporter-based screen would have been inappropriate to identify novel regulators of ES cell differentiation. To overcome the problems, we have developed a unique signature-based screen. This screen is capable of analyzing the expression of 48 genes simultaneously across dozens of different samples, and our gene list covers all three germ layers that arise during normal embryonic development, the trophectoderm, and epigenetic regulators of chromatin status. Our signature-based screen established several categories of genes based on their comparative functions during the differentiation of ES cells. This will be a valuable information for other researchers interested in ES cell differentiation from various perspectives. We have identified two novel regulators of ES cell differentiation – Yap1 and Rbpj. Yap1 is a transcriptional co-activator of Hippo signaling pathway. We disproved past misconceptions in the field about the role of Yap1 concerning its function in ES cell self-renewal, showing that like the inner cell mass, Yap1 is dispensable for long-term maintenance in culture. Conversely, we found that Yap1 is essential for proper ES cell differentiation. Rbpj is a transcriptional regulator of Notch signaling pathway. Consistent with previous observations of repressive role of Rbpj, Rbpj serves as a repressor of ES cell core factors in the absence of Notch signaling pathway. Repressive role of Rbpj is also required for proper differentiation of ES cells by silencing core factors.Cellular and Molecular Biolog

Tgif1 Counterbalances The Activity Of Core Pluripotency Factors In Mouse Embryonic Stem Cells

Core pluripotency factors, such as Oct4, Sox2, and Nanog, play important roles in maintaining embryonic stem cell (ESC) identity by autoregulatory feedforward loops. Nevertheless, the mechanism that provides precise control of the levels of the ESC core factors without indefinite amplification has remained elusive. Here, we report the direct repression of core pluripotency factors by Tgif1, a previously known terminal repressor of TGF beta/activin/nodal signaling. Overexpression of Tgif1 reduces the levels of ESC core factors, whereas its depletion leads to the induction of the pluripotency factors. We confirm the existence of physical associations between Tgif1 and Oct4, Nanog, and HDAC1/2 and further show the level of Tgif1 is not significantly altered by treatment with an activator/inhibitor of the TGF beta/activin/nodal signaling. Collectively, our findings establish Tgif1 as an integral member of the core regulatory circuitry of mouse ESCs that counterbalances the levels of the core pluripotency factors in a TGF beta/activin/nodal-independent manner.Cancer Prevention Research Institute of Texas (CPRIT) R1106Molecular Bioscience

Applying synthetic biology and computational biology to advance biologics expression platforms

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Lessons from Bilski

In this paper, I will examine how the U.S. and Canadian courts have approached the patentability of intangible inventions and discuss whether any lessons can be learned from the U.S.’s patent dilemma. In section 2, I will review the American jurisprudence on patentability of intangible inventions. In section 3, I will discuss the potential impact Bilski may have on the American jurisprudence. Section 4 will assess the Canadian jurisprudence on patentability of intangible inventions. In section 5, I will discuss the Federal Court of Canada’s decision in Amazon/FCC. I argue that based on recent events in the American jurisprudence, Canadian courts should carefully consider the consequences of opening up patent protection to intangible inventions because granting too much patent protection can impede innovation and endanger the patent system

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates