Chromatin Preparation and Chromatin Immuno-precipitation from Drosophila Embryos

This protocol provides specific details on how to perform Chromatin immunoprecipitation (ChIP) from Drosophila embryos. ChIP allows the matching of proteins or histone modifications to specific genomic regions. Formaldehyde-cross-linked chromatin is isolated and antibodies against the target of interest are used to determine whether the target is associated with a specific DNA sequence. This can be performed in spatial and temporal manner and it can provide information about the genome-wide localization of a given protein or histone modification if coupled with deep sequencing technology (ChIP-Seq)

Southern Sky Redshift Survey: Clustering of Local Galaxies

We use the two-point correlation function to calculate the clustering properties of the recently completed SSRS2 survey. The redshift space correlation function for the magnitude-limited SSRS2 is given by xi(s)=(s/5.85 h-1 Mpc)^{-1.60} for separations between 2 < s < 11 h-1 Mpc, while our best estimate for the real space correlation function is xi(r) = (r/5.36 h-1 Mpc)^{-1.86}. Both are comparable to previous measurements using surveys of optical galaxies over much larger and independent volumes. By comparing the correlation function calculated in redshift and real space we find that the redshift distortion on intermediate scales is small. This result implies that the observed redshift-space distribution of galaxies is close to that in real space, and that beta = Omega^{0.6}/b < 1, where Omega is the cosmological density parameter and b is the linear biasing factor for optical galaxies. We also use the SSRS2 to study the dependence of xi on the internal properties of galaxies. We confirm earlier results that luminous galaxies (L>L*) are more clustered than sub-L* galaxies and that the luminosity segregation is scale-independent. We find that early types are more clustered than late types, but that in the absence of rich clusters, the relative bias between early and late types in real space, is not as strong as previously estimated. Furthermore, both morphologies present a luminosity-dependent bias, with the early types showing a slightly stronger dependence on luminosity. We also find that red galaxies are significantly more clustered than blue ones, with a mean relative bias stronger than that seen for morphology. Finally, we find that the relative bias between optical and iras galaxies in real space is b_o/b_I $\sim$ 1.4.Comment: 43 pages, uses AASTeX 4.0 macros. Includes 8 tables and 16 Postscript figures, updated reference

Impact of Orbital Parameters and Greenhouse Gas on the Climate of MIS 7 and MIS 5 Glacial Inceptions

This work explores the impact of orbital parameters and greenhouse gas concentrations on the climate of marine isotope stage (MIS) 7 glacial inception and compares it to that of MIS 5. The authors use a coupled atmosphere-ocean general circulation model to simulate the mean climate state of six time slices at 115, 122, 125, 229, 236, and 239 kyr, representative of a climate evolution from interglacial to glacial inception conditions. The simulations are designed to separate the effects of orbital parameters from those of greenhouse gas (GHG). Their results show that, in all the time slices considered, MIS 7 boreal lands mean annual climate is colder than the MIS 5 one. This difference is explained at 70% by the impact of the MIS 7 GHG. While the impact of GHG over Northern Hemisphere is homogeneous, the difference in temperature between MIS 7 and MIS 5 due to orbital parameters differs regionally and is linked with the Arctic Oscillation. The perennial snow cover is larger in all the MIS 7 experiments compared to MIS 5, as a result of MIS 7 orbital parameters, strengthened by GHG. At regional scale, Eurasia exhibits the strongest response to MIS 7 cold climate with a perennial snow area 3 times larger than in MIS 5 experiments. This suggests that MIS 7 glacial inception is more favorable over this area than over North America. Furthermore, at 239 kyr, the perennial snow covers an area equivalent to that of MIS 5 glacial inception (115 kyr). The authors suggest that MIS 7 glacial inception is more extensive than MIS 5 glacial inception over the high latitudes

Diclofenac sorption from synthetic water: Kinetic and thermodynamic analysis

This work investigated diclofenac sorption on 0.5g L-1 activated carbon in a range of temperature (288-318K) and of initial sorbate concentration (24-218mgL-1). Thermodynamic modelling was carried out with the Langmuir isotherm. For kinetic modelling we compared the so-called Diffusion-Controlled Langmuir Kinetics (DCLK) and the pseudo-second order (PSO) model. The maximum sorption capacity of the sorbent, equal to 180mgg-1, was independent of temperature. Experimental data fitted well with both kinetic models, yet the DCLK model was found to be more informative about the mechanism of the process. Kinetic parameters (α, β) increased with the temperature, with α value rising from 5×10-5 to 20×10-5 L mg-1min-0.5, and β value rising from 3×10-6 to 20×10-6 L mg-1min-1 in the temperature range investigated

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region