Changes in ROS (A) and LPO (B) levels of <i>C. vulgaris</i> during cultivation time of day 0, 2, 4 and 6 at different Se concentrations.

<p>The single asterisk * in the figure represents significant difference compared to control (<i>P<</i>0.05). The • in figure means significant difference of high Se treatments (100–200 mg L⁻¹) compared to low Se treatments (25–75 mg L⁻¹) and control (<i>P<</i>0.05). Values expressed are means ± <i>SD</i> of three replicates.</p

Changes in photosynthetic pigments of <i>C. vulgaris</i> during cultivation time of day 0, 2, 4 and 6 at different Se concentrations.

<p>(A) Carotenoids (including lutein and β-carotene). (B) Chlorophyll a. The single asterisk * in the figure represents significant difference compared to control (<i>P<</i>0.05). Values expressed are means ± <i>SD</i> of three replicates.</p

Effect of Se at different concentrations on biomass concentration (A) and relative growth rate (B).

<p>The single asterisk * in the figure represents significant difference compared to control (<i>P<</i>0.05). Values expressed are means ± <i>SD</i> of three replicates.</p

Changes in antioxidant enzyme activities of <i>C. vulgaris</i> during cultivation time at different Se concentrations.

<p>(A) GPX, (B) CAT, (C) SOD. Values expressed are means ± <i>SD</i> of three replicates.</p

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol–1 K–1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures

Spectroscopic Study of Anisotropic Excitons in Single Crystal Hexacene

The linear optical response of hexacene single crystals over a spectral range of 1.3–1.9 eV was studied using polarization-resolved reflectance spectroscopy at cryogenic temperatures. We observe strong polarization anisotropy for all optical transitions. Pronounced deviations from the single-molecule, solution-phase spectra are present, with a measured Davydov splitting of 180 meV, indicating strong intermolecular coupling. The energies and oscillator strengths of the relevant optical transitions and polarization-dependent absorption coefficients are extracted from quantitative analysis of the data

Data_Sheet_1_Preventing P-gp Ubiquitination Lowers Aβ Brain Levels in an Alzheimer’s Disease Mouse Model.PDF

<p>One characteristic of Alzheimer’s disease (AD) is excessive accumulation of amyloid-β (Aβ) in the brain. Aβ brain accumulation is, in part, due to a reduction in Aβ clearance from the brain across the blood-brain barrier. One key element that contributes to Aβ brain clearance is P-glycoprotein (P-gp) that transports Aβ from brain to blood. In AD, P-gp protein expression and transport activity levels are significantly reduced, which impairs Aβ brain clearance. The mechanism responsible for reduced P-gp expression and activity levels is poorly understood. We recently demonstrated that Aβ₄₀ triggers P-gp degradation through the ubiquitin-proteasome pathway. Consistent with these data, we show here that ubiquitinated P-gp levels in brain capillaries isolated from brain samples of AD patients are increased compared to capillaries isolated from brain tissue of cognitive normal individuals. We extended this line of research to in vivo studies using transgenic human amyloid precursor protein (hAPP)-overexpressing mice (Tg2576) that were treated with PYR41, a cell-permeable, irreversible inhibitor of the ubiquitin-activating enzyme E1. Our data show that inhibiting P-gp ubiquitination protects the transporter from degradation, and immunoprecipitation experiments confirmed that PYR41 prevented P-gp ubiquitination. We further found that PYR41 treatment prevented reduction of P-gp protein expression and transport activity levels and substantially lowered Aβ brain levels in hAPP mice. Together, our findings provide in vivo proof that the ubiquitin-proteasome system mediates reduction of blood-brain barrier P-gp in AD and that inhibiting P-gp ubiquitination prevents P-gp degradation and lowers Aβ brain levels. Thus, targeting the ubiquitin-proteasome system may provide a novel therapeutic approach to protect blood-brain barrier P-gp from degradation in AD and other Aβ-based pathologies.</p

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol–1 K–1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures

Measurement of Minute Liquid Volumes of Chiral Molecules Using In-Fiber Polarimetry

We report an optofluidic method that enables to efficiently measure the enantiomeric excess of chiral molecules at low concentrations. The approach is to monitor the optical activity induced by a Kagome-lattice hollow-core photonic crystal fiber filled with a sub-μL volume of chiral compounds. The technique also allows monitoring the enzymatic racemization of R-mandelic acid

P‑gp Protein Expression and Transport Activity in Rodent Seizure Models and Human Epilepsy

A cure for epilepsy is currently not available, and seizure genesis, seizure recurrence, and resistance to antiseizure drugs remain serious clinical problems. Studies show that the blood–brain barrier is altered in animal models of epilepsy and in epileptic patients. In this regard, seizures increase expression of blood–brain barrier efflux transporters such as P-glycoprotein (P-gp), which is thought to reduce brain uptake of antiseizure drugs, and thus, contribute to antiseizure drug resistance. The goal of the current study was to assess the viability of combining <i>in vivo</i> and <i>ex vivo</i> preparations of isolated brain capillaries from animal models of seizures and epilepsy as well as from patients with epilepsy to study P-gp at the blood–brain barrier. Exposing isolated rat brain capillaries to glutamate <i>ex vivo</i> upregulated P-gp expression to levels that were similar to those in capillaries isolated from rats that had status epilepticus or chronic epilepsy. Moreover, the fold-increase in P-gp protein expression seen in animal models is consistent with the fold-increase in P-gp observed in human brain capillaries isolated from patients with epilepsy compared to age-matched control individuals. Overall, the <i>in vivo</i>/<i>ex vivo</i> approach presented here allows detailed analysis of the mechanisms underlying seizure-induced changes of P-gp expression and transport activity at the blood–brain barrier. This approach can be extended to other blood–brain barrier proteins that might contribute to drug-resistant epilepsy or other CNS disorders as well