Toward more effective gene delivery

A report on the symposium 'In vivo barriers to gene delivery', Cold Spring Harbor, USA, 26-29 November 2007

The Role of Autophagy During Myocardial Ischemia/Reperfusion Injury

Autophagy is a housekeeping process to remove damaged cytoplasmic constituents. However, a debate persists on whether autophagy is beneficial or detrimental when an ischemic/reperfusion (I/R) insult occurs in the heart. This study tested the effects of autophagy enhancers (e.g. rapamycin and trehalose) and autophagy inhibitor (e.g. 3-methyladenine) on heart function and infarct size after global I (30 minutes) and R (45 minutes) when given prior to ischemia (pre-treatment) or at the beginning of reperfusion (post-treatment). We found that Rapamycin (25nM) pre-treatment and post-treatment significantly restored final left ventricular developed pressure (LVDP) to 75.4±9.1% and 60±5% of initial baseline respectively (both n=5, p\u3c0.05), compared to I/R group (n=9) that recovered to 35±5.5% of initial baseline. Likewise, trehalose (5mM) pre-treatment and post-treatment also significantly restored final LVDP to 61.4±3.7% (n=6) and 69.1±2.7% (n=5) of initial baseline respectively, compared to I/R group. However, 3-methyladenine (1mM) pre-treatment (n=6) and post-treatment (n=5) showed similar reduction in final LVDP to 24.7±9.1% and 33.4±12.8 % of initial baseline respectively, as I/R group. Moreover, infarction percentage was significantly reduced by rapamycin pre-treatment and post-treatment (14 ± 2.8% and 21.4 ± 5.3%, respectively; both p\u3c0.05); and trehalose pre-treatment and post-treatment (19.2 ± 3% and 15.2% ± 3, respectively; both p\u3c0.05), but not by 3-methyladenine pre-treatment and post-treatment (26±2% and 28±4.1%, respectively) when compared to I/R group (38.6±4.3%). The data suggests that autophagy enhancement before ischemia or at reperfusion is beneficial for reducing I/R injury

Diamond-nitrogen-vacancy electronic and nuclear spin-state anticrossings under weak transverse magnetic fields

We report on detailed studies of electronic and nuclear spin states in the diamond-nitrogen-vacancy (NV) center under weak transverse magnetic fields. We numerically predict and experimentally verify a previously unobserved NV hyperfine level anticrossing (LAC) occurring at bias fields of tens of gauss—two orders of magnitude lower than previously reported LACs at ∼ 500 and ∼ 1000 G axial magnetic fields. We then discuss how the NV ground-state Hamiltonian can be manipulated in this regime to tailor the NV's sensitivity to environmental factors and to map into the nuclear spin state.United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contract No. FA8721-05-C-0002)United States. Office of Naval Research (N00014-13-1-0316)United States. National Aeronautics and Space Administration ( Office of the Chief Technologist’s Space Technology Research Fellowship

Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach

Implied volatility (IV) forecasting is inherently challenging due to its high dimensionality across various moneyness and maturity, and nonlinearity in both spatial and temporal aspects. We utilize implied volatility surfaces (IVS) to represent comprehensive spatial dependence and model the nonlinear temporal dependencies within a series of IVS. Leveraging advanced kernel-based machine learning techniques, we introduce the functional Neural Tangent Kernel (fNTK) estimator within the Nonlinear Functional Autoregression framework, specifically tailored to capture intricate relationships within implied volatilities. We establish the connection between fNTK and kernel regression, emphasizing its role in contemporary nonparametric statistical modeling. Empirically, we analyze S&P 500 Index options from January 2009 to December 2021, encompassing more than 6 million European calls and puts, thereby showcasing the superior forecast accuracy of fNTK.We demonstrate the significant economic value of having an accurate implied volatility forecaster within trading strategies. Notably, short delta-neutral straddle trading, supported by fNTK, achieves a Sharpe ratio ranging from 1.45 to 2.02, resulting in a relative enhancement in trading outcomes ranging from 77% to 583%

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.</p

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.</p