A Review on the Visible Light Active Titanium Dioxide Photocatalysts for Environmental Applications

Development of visible light active (VLA) titania photocatalysts Fujishima and Honda (1972) demonstrated the potential of titanium dioxide (TiO 2) semiconductor mate-rials to split water into hydrogen and oxygen in a photo-electrochemical cell. Their work triggered the development of semiconductor photocatalysis for a wide range of environmental and energy applica-tions. One of the most significant scientific and commercial advances to date has been the development of visible light active (VLA) TiO2 photocatalytic materials. In this review, a background on TiO2 struc-ture, properties and electronic properties in photocatalysis is presented. The development of different strategies to modify TiO2 for the utilization of visible light, including non metal and/or metal doping, dye sensitization and coupling semiconductors are discussed. Emphasis is given to the origin of visible light absorption and the reactive oxygen species generated, deduced by physicochemical and photo-electrochemical methods. Various applications of VLA TiO2, in terms of environmental remediation and in particular water treatment, disinfection and air purification, are illustrated. Comprehensive studies on the photocatalytic degradation of contaminants of emerging concern, including endocrine disrupting compounds, pharmaceuticals, pesticides, cyanotoxins and volatile organic compounds, with VLA TiO2 are discussed and compared to conventional UV-activated TiO2 nanomaterials. Recent advances in bac-terial disinfection using VLA TiO2 are also reviewed. Issues concerning test protocols for real visible light activity and photocatalytic efficiencies with different light sources have been highlighted

High dietary fat intake increases fat oxidation and reduces skeletal muscle mitochondrial respiration in trained humans.

High-fat, low-carbohydrate (CHO) diets increase whole-body rates of fat oxidation and down-regulate CHO metabolism. We measured substrate utilization and skeletal muscle mitochondrial respiration to determine whether these adaptations are driven by high fat or low CHO availability. In a randomized crossover design, 8 male cyclists consumed 5 d of a high-CHO diet [>70% energy intake (EI)], followed by 5 d of either an isoenergetic high-fat (HFAT; >65% EI) or high-protein diet (HPRO; >65% EI) with CHO intake clamped at <20% EI. During the intervention, participants undertook daily exercise training. On d 6, participants consumed a high-CHO diet before performing 100 min of submaximal steady-state cycling plus an ∼30-min time trial. After 5 d of HFAT, skeletal muscle mitochondrial respiration supported by octanoylcarnitine and pyruvate, as well as uncoupled respiration, was decreased at rest, and rates of whole-body fat oxidation were higher during exercise compared with HPRO. After 1 d of high-CHO diet intake, mitochondrial respiration returned to baseline values in HFAT, whereas rates of substrate oxidation returned toward baseline in both conditions. These findings demonstrate that high dietary fat intake, rather than low-CHO intake, contributes to reductions in mitochondrial respiration and increases in whole-body rates of fat oxidation after a consuming a high-fat, low-CHO diet.-Leckey, J. J., Hoffman, N. J., Parr, E. B., Devlin, B. L., Trewin, A. J., Stepto, N. K., Morton, J. P., Burke, L. M., Hawley, J. A. High dietary fat intake increases fat oxidation and reduces skeletal muscle mitochondrial respiration in trained humans

Combined constraints on modified Chaplygin gas model from cosmological observed data: Markov Chain Monte Carlo approach

We use the Markov Chain Monte Carlo method to investigate a global constraints on the modified Chaplygin gas (MCG) model as the unification of dark matter and dark energy from the latest observational data: the Union2 dataset of type supernovae Ia (SNIa), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. In a flat universe, the constraint results for MCG model are, $\Omega_{b}h^{2}=0.02263^{+0.00184}_{-0.00162}$ ($1\sigma$) $^{+0.00213}_{-0.00195}$ $(2\sigma)$, $B_{s}=0.7788^{+0.0736}_{-0.0723}$ ($1\sigma$) $^{+0.0918}_{-0.0904}$ $(2\sigma)$, $\alpha=0.1079^{+0.3397}_{-0.2539}$ ($1\sigma$) $^{+0.4678}_{-0.2911}$ $(2\sigma)$, $B=0.00189^{+0.00583}_{-0.00756}$ ($1\sigma$) $^{+0.00660}_{-0.00915}$ $(2\sigma)$, and $H_{0}=70.711^{+4.188}_{-3.142}$ ($1\sigma$) $^{+5.281}_{-4.149}$ $(2\sigma)$.Comment: 12 pages, 1figur

Does accelerating universe indicates Brans-Dicke theory

The evolution of universe in Brans-Dicke (BD) theory is discussed in this paper. Considering a parameterized scenario for BD scalar field $\phi=\phi_{0}a^{\alpha}$ which plays the role of gravitational "constant" $G$, we apply the Markov Chain Monte Carlo method to investigate a global constraints on BD theory with a self-interacting potential according to the current observational data: Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs) data, observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. It is shown that an expanded universe from deceleration to acceleration is given in this theory, and the constraint results of dimensionless matter density $\Omega_{0m}$ and parameter $\alpha$ are, $\Omega_{0m}=0.286^{+0.037+0.050}_{-0.039-0.047}$ and $\alpha=0.0046^{+0.0149+0.0171}_{-0.0171-0.0206}$ which is consistent with the result of current experiment exploration, $\mid\alpha\mid \leq 0.132124$. In addition, we use the geometrical diagnostic method, jerk parameter $j$, to distinguish the BD theory and cosmological constant model in Einstein's theory of general relativity.Comment: 16 pages, 3 figure

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease