DIFFERENTIAL STABILIZING EFFECTS OF BUFFERS ON STRUCTURAL STABILITY OF BOVINE SERUM ALBUMIN AGAINST UREA DENATURATION

The conformational stability of bovine serum albumin (BSA) against urea denaturation was investigated in aqueous solutions both in the absence and presence of buffers. Various buffers differing in polar and nonpolar characters such as sodium phosphate, Tris-HCl, (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) HEPES and [3-(N-morpholino)propanesulfonic acid] MOPS buffers were used in this study. Urea-induced structural changes were analyzed using different probes, i.e., intrinsic fluorescence, ANS fluorescence and UV-difference spectral signal. Presence of different buffers in the incubation medium offered different degrees of resistance to the protein against urea-induced structural changes compared to those obtained in water (in the absence of buffers). A similar trend of buffer-induced structural resistance was noticed with three different probes. The stabilizing effect of these buffers followed the order: MOPS > HEPES > sodium phosphate > Tris-HCl > water. As found in MOPS and HEPES buffers, the highest stability of BSA can be attributed to the presence of morpholine and piperazine rings, respectively, in their structures. These groups might have produced a hydrophobic environment around the protein surface, thus stabilizing protein conformation against urea denaturation.

Tracking excited state decay mechanisms of pyrimidine nucleosides in real time

DNA owes its remarkable photostability to its building blocks—the nucleosides—that efficiently dissipate the energy acquired upon ultraviolet light absorption. The mechanism occurring on a sub-picosecond time scale has been a matter of intense debate. Here we combine sub-30-fs transient absorption spectroscopy experiments with broad spectral coverage and state-of-the-art mixed quantum-classical dynamics with spectral signal simulations to resolve the early steps of the deactivation mechanisms of uridine (Urd) and 5-methyluridine (5mUrd) in aqueous solution. We track the wave packet motion from the Franck-Condon region to the conical intersections (CIs) with the ground state and observe spectral signatures of excited-state vibrational modes. 5mUrd exhibits an order of magnitude longer lifetime with respect to Urd due to the solvent reorganization needed to facilitate bulky methyl group motions leading to the CI. This activates potentially lesion-inducing dynamics such as ring opening. Involvement of the 1nπ* state is found to be negligible.

Why can we detect lianas from space?

Lianas are found in virtually all tropical forests and have strong impacts on the forest carbon cycle by slowing tree growth, increasing tree mortality and arresting forest succession. In a few local studies, ecologists have successfully differentiated lianas from trees using various remote sensing platforms including satellite images. This demonstrates a potential to use remote sensing to investigate liana dynamics at spatio-temporal scales beyond what is currently possible with ground-based inventory censuses. However, why do liana-infested tree crowns and forest stands display distinct spectral signals? And is the spectral signal of lianas only locally unique or consistent across continental and global scales? Unfortunately, we are not yet able to answer these questions, and without such an understanding the limitations and caveats of large-scale application of automated classifiers cannot be understood. Here, we tackle the questions of why we can detect lianas from airborne and spaceborne remote sensing platforms. We identify whether a distinct spectral distribution exists for lianas, when compared to their tree hosts, at the leaf, canopy and stand scales in the solar spectrum (400 to 2500 nm). To do so, we compiled databases of (i) leaf reflectance spectra for over 4771 individual leaves of 571 species, (ii) fine-scale (~1m2) surface reflectance from 999 tree canopies characterized by different levels of liana infestation in Panama and Malaysia, and (iii) coarse-scale (>100 m2) surface reflectance from hundreds of hectares of heavily infested liana forest stands in French Guiana and Bolivia. Using these data, we find consistent spectral signal of liana-infested canopies across sites with a mean inter-site correlation of 89% (range 74-94%). However, as we find no consistent difference between liana and tree leaves, a distinct liana spectral signal appears to only manifests at the canopy and stand scales (>1m2). To better understand this signal, we implement mechanistic radiative transfer models capable of modeling the vertically stratificatied non-linear mixing of spectral signals intrinsic to lianas infestation of forest canopies. Next, we inversely fit the models to observed spectral signals of lianas at all scales to identify key biochemical or biophysical processes. We then corroborate our model results with field data on liana leaf chemistry and canopy structural properties. Our results suggest that a liana-specific spectral distribution arises due to the combination of cheaply constructed leaves and efficient light interception. A model experiment revealed that the spectral distribution was most sensitive to lower leaf and water mass per unit area, affecting the absorption of NIR and SWIR radiation, and a more planophile (flatter) leaf angle distribution. Finally, we evaluate the theoretical discernibility of lianas from trees and how this varies with remote sensing platforms and resolution. We end by discussing the potential, limitations and risks of applying automated classifiers to detect lianas from remotely sensed data at large scales.

Brief segments of neurophysiological activity enable individual differentiation

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual’s neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

Efficient Detection of Multiple FBG Wavelength Peaks using Matched Filtering Technique

We propose an efficient technique for FBG peak detection based on matched filtering technique. The matched filtering process is based on resonance point estimation between a standard reference spectral signal and a reflected spectrum of FBG. The desired peak wavelength and corresponding peak intensity are predicted by determining of the cross-correlation between the FBG signal and 3rd derivative of the reference signal. The peak wavelength and intensity are found from the zero-crossing points of the cross-correlation function. The Mexican-hat wavelet function is chosen as the reference spectral signal due to its narrow shape. The proposed algorithm can suitably be used for multiple peak detection when several FBGs are cascaded and if the FBG signal is weak and noisy.

A Nonlinear Integrated Modeling Method of Extended Kalman Filter Based on Adaboost Algorithm

In the zinc hydrometallurgical purification process, the concentration ratio of zinc ion to trace nickel ion is as high as 105, so that the nickel spectral signal is completely covered by high concentration zinc signal, resulting in low sensitivity and nonlinear characteristics of nickel spectral signal. Aiming at the problem that it is difficult to detect nickel in zinc sulfate solution, this paper proposes a nonlinear integrated modeling method of extended Kalman filter based on Adaboost algorithm. First, a non-linear nickel model is established based on nickel standard solution. Second, an extended Kalman filter wavelength optimization method based on correlation coefficient is proposed to select wavelength variables with high signal sensitivity, large amount of information and strong nonlinear correlation. Finally, a nonlinear integrated modeling method based on Adaboost algorithm is proposed, which uses extended Kalman filter as a basic submodel, and realizes the stable detection of trace nickel through the weighted combination of multiple basic models. The results show that the average relative error of this method for detecting nickel is 4.56%, which achieves accurate detection of trace nickel in zinc sulfate solution.

Anatomy of a cascading hazard: the flood part of the 7 February Uttarakhand event

<p>The 7 February Chamoli, Uttarakhand singularity imposed a severe geomorphic crisis. While remote sensing imagery quickly identified a major rock avalanche as its origin, there is a fundamental lack in high precision temporal information on the kinetics of this event about when, how, and why it evolved from a slope failure into a channel-confined mass wasting process, and ultimately into a debris laden flood. Furthermore, while the initial rock slide could be detected and located by global seismic networks, it was the flood which caused most of the destruction and fatalities. Yet, that part of the process cascade remained elusive in global seismic data sets.</p><p>Here, we present a detailed anatomy of the hazard cascade, with emphasis on the flood part. Using information from a dense seismic network, we explore the limits of detection and constrain its propagation velocity. By jointly inverting two physical models that predict spectral signal properties of floods, we estimate important hydraulic and sediment transport metrics. These information are key for designing any future early warning infrastructure.</p>