Effect of cholecalciferol on unsaturated model membranes

Vitamin D plays an important role in many physiological processes, particularly calcium and phosphorous homeostasis. The biochemistry of vitamin D is also complex, encompassing a range of active molecules that may be either endogenous or dietary in origin. The role of lipids and fats in the production, processing and use of vitamin D is an interesting one, with a relative paucity of model studies into the interactions of vitamin D with lipidic systems such as micelles and vesicles. Here, we have studied the effect of vitamin D3 in simple unsaturated phospholipid systems. We used NMR and FTIR spectroscopy to investigate the effect of increasing vitamin D concentration on the structure and dynamics of the lipid chains and interfacial region. In order to link these model studies with more complex biomimetic environments, we compare results in the presence of buffer and vitamin D binding protein. We have also used DLS to determine that vitamin D3-DOPC vesicles can retain their size distribution for varying amounts of time in different conditions. We find that the acyl chain region of vitamin D3-DOPC membranes are generally disordered, and that the addition of buffer and/or protein alters the properties of the interfacial region

Evaluation of coarse-grained mapping schemes for polysaccharide chains in cellulose

A fundamental understanding of the intermolecular forces that bind polysaccharide chains together in cellulose is crucial for designing efficient methods to overcome the recalcitrance of lignocellulosic biomass to hydrolysis. Because the characteristic time and length scales for the degradation of cellulose by enzymatic hydrolysis or chemical pretreatment span orders of magnitude, it is important to closely integrate the molecular models used at each scale so that, ultimately, one may switch seamlessly between quantum, atomistic, and coarse-grained descriptions of the system. As a step towards that goal, four multiscale coarse-grained models for polysaccharide chains in a cellulose-Iα microfiber are considered. Using the force matching method, effective coarse-grained forces are derived from all-atom trajectories. Performance of the coarse-grained models is evaluated by comparing the intrachain radial distribution functions with those obtained using the all-atom reference data. The all-atom simulation reveals a double peak in the radial distribution function for sites within each glucose residue that arises from the distinct conformations sampled by the primary alcohol group in the glucose residues. The three-site and four-site coarse-grained models have sufficient degrees of freedom to predict this double peak while the one-site and two-site models do not. This is the first time that coarse-grained models have been shown to reproduce such subtle, yet important, molecular features in a polysaccharide chain. The relative orientations between glucose residues along the polysaccharide chain are evaluated and it is found that the four-site coarse-grained model is best at reproducing the glucose-glucose conformations observed in the all-atom simulation. The success of the four-site coarse-grained model underscores the importance of decoupling the pyranose ring from the oxygen atom in the glycosidic bond when developing all-atom to coarse-grained mapping schemes for polysaccharides

Layered, Tunable Graphene Oxide-Nylon Heterostructures for Wearable Electrocardiogram Sensors

Nanoscale engineered materials combined with wearable wireless technologies can deliver a new level of health monitoring. A reduced graphene oxide-nylon composite material is developed and tested, demonstrating its usefulness as a material for sensors in wearable, long-term electrocardiogram (ECG) monitoring via a comparison to one of the widely used ECG sensors. The structural analysis by scanning electron (SEM) and atomic force microscopy (AFM) shows a limited number of defects on a macroscopic scale. Fourier Transform Infrared (FTIR) and Raman spectroscopy confirm the presence of rGOx, and the ratio of D- and G-features as a function of thickness correlates with the resistivity analysis. The negligible effect of the defects and the tunability of electrical and optical properties, together with live ECG data, demonstrate its signal transduction capability.Comment: 7 main text and 4 supporting figures, under revie

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Urinary nitrate might be an early biomarker for pediatric acute kidney injury in the emergency department

NO is involved in normal kidney function and perturbed in acute kidney injury (AKI). We hypothesized that urinary concentration of NO metabolites, nitrite, and nitrate would be lower in children with early AKI presenting to the emergency department (ED), when serum creatinine (SCr) was uninformative. Patients up to 19 y were recruited if they had a urinalysis and SCr obtained for routine care. Primary outcome, AKI, was defined by pediatric Risk, Injury, Failure, Loss of function, End-stage renal disease (pRIFLE) criteria. Urinary nitrite and nitrate were determined by HPLC. A total of 252 patients were enrolled, the majority (93%) of whom were without AKI. Although 18 (7%) had AKI by pRIFLE, 50% may not have had it identified by the SCr value alone at the time of visit. Median urinary nitrate was lower for injury versus risk (p = 0.03); this difference remained significant when the injury group was compared against the combined risk and no AKI groups (p = 0.01). Urinary nitrite was not significantly different between groups. Thus, low urinary nitrate is associated with AKI in the pediatric ED even when SCr is normal. Predictive potential of this putative urinary biomarker for AKI needs further evaluation in sicker patients

Financial crises and the attainment of the SDGs: an adjusted multidimensional poverty approach

This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 indicators that span across key poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty indicators, warn of a negative domino effect on a number of SDGs related to poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community

Tasking networked CCTV cameras and mobile phones to identify and localize multiple people

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person’s mobile phones. Since a person’s motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones ’ IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a twomeasurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed