The use of a quantitative structure-activity relationship (QSAR) model to predict GABA-A receptor binding of newly emerging benzodiazepines

The illicit market for new psychoactive substances is forever expanding. Benzodiazepines and their derivatives are one of a number of groups of these substances and thus far their number has grown year upon year. For both forensic and clinical purposes it is important to be able to rapidly understand these emerging substances. However as a consequence of the illicit nature of these compounds, there is a deficiency in the pharmacological data available for these ‘new’ benzodiazepines. In order to further understand the pharmacology of ‘new’ benzodiazepines we utilised a quantitative structure-activity relationship (QSAR) approach. A set of 69 benzodiazepine-based compounds was analysed to develop a QSAR training set with respect to published binding values to GABAA receptors. The QSAR model returned an R2 value of 0.90. The most influential factors were found to be the positioning of two H-bond acceptors, two aromatic rings and a hydrophobic group. A test set of nine random compounds was then selected for internal validation to determine the predictive ability of the model and gave an R2 value of 0.86 when comparing the binding values with their experimental data. The QSAR model was then used to predict the binding for 22 benzodiazepines that are classed as new psychoactive substances. This model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with lengthy and expensive in vitro/in vivo analysis. This will enable forensic chemists and toxicologists to better understand both recently developed compounds and prediction of substances likely to emerge in the future

Supervised clustering of streaming data for email batch detection

We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made – owing to the streaming nature of the data – then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails. 1

Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction

The paper uses a frame-theoretic setting to study the injectivity of a ReLU-layer on the closed ball of $\mathbb{R}^n$ and its non-negative part. In particular, the interplay between the radius of the ball and the bias vector is emphasized. Together with a perspective from convex geometry, this leads to a computationally feasible method of verifying the injectivity of a ReLU-layer under reasonable restrictions in terms of an upper bound of the bias vector. Explicit reconstruction formulas are provided, inspired by the duality concept from frame theory. All this gives rise to the possibility of quantifying the invertibility of a ReLU-layer and a concrete reconstruction algorithm for any input vector on the ball.Comment: 10 pages main paper + 2 pages appendix, 4 figures, 2 algorithms, conferenc

Integrated dataset on acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin

Data herein describe the quantitative changes in the plasma proteome in chickens challenged with lipopolysaccharide (LPS), a bacterial endotoxin known to stimulate the host innate immune system obtained by shotgun quantitative proteomic tandem mass tags approach using high-resolution Orbitrap technology. Statistical and bioinformatic analyses were performed to specify the effect of bacterial endotoxin. Plasma from chicken (N=6) challenged with Escherichia coli (LPS) (2 mg/kg body weight) was collected pre (0 h) and at 12, 24, 48, and 72 h post injection along with plasma from a control group (N=6) challenged with sterile saline. Protein identification and relative quantification were performed using Proteome Discoverer, and data were analysed using R. Gene Ontology terms were analysed by the Cytoscape application ClueGO based on Gallus gallus GO Biological Process database, and refined by REVIGO. Absolute quantification of several acute phase proteins, e.g. alpha-1-acid glycoprotein (AGP), serum amyloid A (SAA) and ovotrensferrin (OVT) was performed by immunoassays to validate the LC-MS results. The data contained within this article are directly related to our research article”Quantitative proteomics using tandem mass tags in relation to the acute phase protein response in chicken challenged with Escherichia coli lipopolysaccharide endotoxin” [1]. The raw mass spectrometric data generated in this study were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009399 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD009399)

Revisiting history: Can shipping achieve a second socio-technical transition for carbon emissions reduction?

This paper draws on socio-technical transitions theory to contextualise recent developments in the technological and operational eco-efficiency of ships, which may ameliorate but not resolve sustainability challenges in shipping. Taking an historical perspective, the paper argues that shipping is fundamentally a derived demand arising out of, but also enabling, the spatial separation of production and consumption that are integrated through global value chains. It is argued that the twin processes of innovation-enabled specialisation (into e.g. container ships; bulk carriers etc.) and increased scale both of ships and of shipping operations have embedded shipping into logistics systems of increasing complexity and reach. The objective of the paper is to demonstrate, using secondary data, the long-run trends in the growth of shipping carbon emissions for bulkers and tankers, as well as the impact of increased scale and vessel speed on such emissions. A fuel-based, top-down, methodology, based on fuel consumption estimates derived from secondary source industry data that are suitable for a macro-level analysis, is used to estimate global shipping carbon emissions. It is argued that technologies or operational innovations that reduce the environmental burdens of shipping, while useful, do not represent the socio-technical system ‘regime’ shift that international maritime logistics requires in order to contribute to improved sustainability. Rather, in the relative absence of strong governance mechanisms in the maritime field, it is underlying ‘landscape’ shifts in production and consumption that are likely to act to reduce the demand for shipping and hence to be more significant in the longer run

Acute phase proteins and stress markers in the immediate response to a combined vaccination against Newcastle disease and infectious bronchitis viruses in specific pathogen free (SPF) layer chicks

Vaccination is an important tool in poultry health, but is itself a stressor often resulting in a reduction in feed intake, body weight gain, and nutrient digestibility. In other species, vaccination is associated with an immediate acute-phase response. As an important immune parameter, the circulating heterophil/lymphocyte (H/L) ratio is a well-recognized parameter of stress in poultry. In this study, the effects of a routinely used commercial poultry vaccine on the acute phase response (APR) and H/L ratios in specific pathogen-free (SPF) layer chicks was examined to determine if post vaccination (PV) stress and an APR occur. A combined Newcastle disease and infectious bronchitis vaccine (Nobalis Ma5+Clone 30) was administered to SPF chicks by the intraocular route at age 7 d. Acute phase proteins (APP), alpha-1 acid glycoprotein (AGP) and serum amyloid A (SAA) were measured by enzyme-linked immunosorbent assays at d 0 (pre-vaccination) and d 0.5, 1, 2, 3, 4, 5, 6, and 21 PV. Stress was determined in the chicks by measurement of the H/L ratio. The immune response to the vaccine was estimated by measurement of the antibody (IgY) response to the vaccine at d 21. The antibody titer was significantly (P < 0.05) higher in the vaccinated group at 21 d PV, confirming stimulation of the immune system. The H/L ratio was also significantly higher in the vaccinated group at 1 to 2 d (P < 0.01) and at 3 d (P < 0.05) PV. The concentration of SAA increased by 2.8-fold, from 63.7 μg/mL in controls to 181 μg/mL in the vaccinated group, (P < 0.05) at 1 d PV. AGP increased 1.6-fold at 2 d PV, (from 0.75 g/mL in the control group to 1.24 g/mL in the vaccinated group, P < 0.05). In conclusion an immediate but mild APR occurred in the chicks following intraocular vaccination, whereas the stress response as measured by H/L ratio seemed to be more specific and sensitive. Measurement of these biomarkers of the host response could be a tool in vaccine development

Instabilities in Convnets for Raw Audio

What makes waveform-based deep learning so hard? Despite numerous attempts at training convolutional neural networks (convnets) for filterbank design, they often fail to outperform hand-crafted baselines. These baselines are linear time-invariant systems: as such, they can be approximated by convnets with wide receptive fields. Yet, in practice, gradient-based optimization leads to suboptimal approximations. In our article, we approach this phenomenon from the perspective of initialization. We present a theory of large deviations for the energy response of FIR filterbanks with random Gaussian weights. We find that deviations worsen for large filters and locally periodic input signals, which are both typical for audio signal processing applications. Numerical simulations align with our theory and suggest that the condition number of a convolutional layer follows a logarithmic scaling law between the number and length of the filters, which is reminiscent of discrete wavelet bases.Comment: 4 pages, 5 figures, 1 page appendix, under review for IEEE SP

The Mayer Hashi Large-Scale Program to Increase Use of Long-Acting Reversible Contraceptives and Permanent Methods in Bangladesh: Explaining the Disappointing Results. An Outcome and Process Evaluation

Background: Bangladesh has achieved a low total fertility rate of 2.3. Two-thirds of currently married women of reproductive age (CMWRA) want to limit fertility, and many women achieve their desired fertility before age 30. The incidence of unintended pregnancy and pregnancy termination is high, however. Long-acting reversible contraceptives (LARCs), consisting of the intrauterine device and implant, and permanent methods (PM), including female sterilization and vasectomy, offer several advantages in this situation, but only 8% of CMWRA or 13% of method users use these methods. Program: The Mayer Hashi (MH) program (2009–2013) aimed to improve access to and the quality of LARC/PM services in 21 of the 64 districts in Bangladesh. It was grounded in the SEED (supply–enabling environment–demand) Programming Model. Supply improvements addressed provider knowledge and skills, system strengthening, and logistics. Creating an enabling environment involved holding workshops with local and community leaders, including religious leaders, to encourage them to help promote demand for LARCs and PMs and overcome cultural barriers. Demand promotion encompassed training of providers in counseling, distribution of behavior change communication materials in the community and in facilities, and community mobilization. Methods: We selected 6 MH program districts and 3 nonprogram districts to evaluate the program. We used a before– after and intervention–comparison design to measure the changes in key contraceptive behavior outcomes, and we used a difference-in-differences (DID) specification with comparison to the nonprogram districts to capture the impact of the program. In addition to the outcome evaluation, we considered intermediate indicators that measured the processes through which the interventions were expected to affect the use of LARCs and PMs. Results: The use of LARCs/PMs among CMWRA increased between 2010 and 2013 in both program (from 5.3% to 7.5%) and nonprogram (from 5.0% to 8.9%) districts, but the rate of change was higher in the nonprogram districts. Client–provider interaction and exposure to LARCs/PMs were lower in the program than nonprogram districts, and the MH program districts had higher vacancies of key providers than the nonprogram areas, both indications of a more difficult health system environment. Conclusion: The weaknesses in the health system in the MH districts apparently undermined the effectiveness of the program. More attention to system weaknesses, such as additional supportive supervision for providers, might have improved the outcome

Data for Heuristic Optimization of Electric Vehicles’ Charging Configuration Based on Loading Parameters

This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge the EVs. The data include lognormally sampled hourly sorted scenarios across 11 charging locations for a stochastics-based Monte Carlo simulation. This simulation runs through 2 million scenarios based on actual probabilities to incorporate most possible situations. It also includes samples from normally distributed household electricity use scenarios based on agent-based modeling. The article includes the test grid parameters for simulation, which were used to create a benchmark grid in DigSilent Powerfactory software, as well as intermediate outputs defining worst case scenarios when electric vehicles were charged and results from three different optimization approaches involving a reduction in voltage drops, cable overloading and total line losses. The outputs from the benchmark grid were used to train a machine learning algorithm, the weights and codes for which are also attached. This trained network acted as the grid for subsequent iterative optimization procedures. Outputs are presented as a comparison between pre-optimization and post-optimization scenarios. The above dataset and procedure were repeated while varying the number of EVs between 0 and 100 in increments of 20, data for which are also attached. The data article supports a related submission titled “Minimization of Overloading Caused by Electric Vehicle (EV) Charging in Low Voltage Networks”. Document type: Articl

Feasibility of Methyl Mercaptane as Probe Molecule for Supported Gold Nanoparticle Surface Area Determination

Gold nanoparticles supported on TiO2 were probed by adsorption of methyl mercaptane (MM), and the process was quantified gravimetrically. This method allowed discrimination between weakly adsorbed (physisorbed) and strongly bound (chemisorbed) methyl mercaptane. Strong adsorption of MM occured on exposed Au faces, while low-temperature pre-treatment (30 °C) completely suppressed adsorption of MM on the TiO2 support. The thus obtained high selectivity of MM adsorption on Au enabled characterization of the gold surface area and the resulting values are comparable with other noble metal systems of similar average particle size. The estimated adsorption stoichiometry indicates that the entire Au surface is probed