Hydrophobic solid surfaces have been found to promote the formation of gas hydrates effectively and thus help to realize the immense potential applications of hydrates in many sectors such as energy supply, gas storage and transportation, gas separation, and CO2 sequestration. Despite the well-known effectiveness, the molecular mechanism behind the promotion effect has not been thoroughly understood. In this work, we used both simulation and experimental means to gain insights into the microscopic level of the influence of hydrophobic solid surfaces on gas hydrate formation. On one hand, our simulation results show the presence of an interfacial gas enrichment (IGE) at hydrophobic surface and a gas depletion layer at hydrophilic surface. In the meantime, the analysis of water structure near the hydrophobic solid interface based on the molecular trajectories also shows that water molecules tend to get locally structured near a hydrophobic surface while becoming depressed near a hydrophilic surface. On the other hand, the experimental results demonstrate the preferential formation of gas hydrate on a hydrophobic surface. The synergic combination of simulation and experimental results points out that the existence of an IGE at hydrophobic solid surface plays a key role in promoting gas hydrate formation. This work advances the molecular level understanding of the role of hydrophobicity in governing the gas hydrate as well as interfacial phenomena in general.

Additives such as surfactants, polymers, salts, and hydrophobic particles are well-known (and used) to influence gas hydrate formation (GHF). This paper reviews and discusses the mechanisms of their effects. The effects of additives on GHF appear to vary greatly from one additive to another. Even a given additive can change from a promoter to an inhibitor and vice versa when the working conditions are changed. The available literature cannot explain the diverse effects of additives. We argue that the hydrophobic effect plays a critical role in gas hydrate formation. A dissolved hydrophobe organizes the surrounding water into a clathrate-like structure and thereby promotes hydrate formation. A hydrophile, however, disrupts the surrounding water structure and inhibits hydrate formation. Moreover, cooperative hydrophobic interactions create an increased gas concentration around a hydrophobe, which also favors the hydrate formation. In contrast, a hydrophile competes with the gas for water and thereby hinders hydrate formation. In particular, when the additive is an amphiphile, the observed effect is the result of the competition between the hydrophobic moiety (a promoter) and hydrophilic moiety (an inhibitor). This hypothesis provides a universal explanation for the various effects of hydrate additives.

Gas hydrates are crystalline solids composed of water and gases. They occur abundantly in nature and are potentially significant to industry. Solid surfaces and confined spaces strongly affect the formation of gas hydrates. Research into this particular topic is active, particularly aiming to understand the effects of solid surfaces and confinements on gas hydrate formation and using functional solids for controlling the formation kinetics. Experimental observations appear to vary from one solid to another. The observations demand a knowledge of (1) why the effects vary among the solids and ( 2) what factors are determining. Here, we critically review experimental observations, discuss the underlying mechanisms, and generalize the literature findings for a better understanding of the mechanism. It is inferred that open hydrophobic solids can promote gas hydrate formation via a tetrahedral ordering of water and an increased density of gases at the solid−water interfaces. Open hydrophilic solids hinder gas hydrate formation via a distorted water structure and a depleted density of gases at the solid−water interfaces. Confining solids have rather complex effects due to the complexity of wetting in confined spaces. Therefore, confined media with moderate wettability and partial water saturation might provide optimum conditions for gas hydrate formation.

BackgroundMicro-blogging services such as Twitter offer the potential to crowdsource epidemics in real-time. However, Twitter posts (‘tweets’) are often ambiguous and reactive to media trends. In order to ground user messages in epidemic response we focused on tracking reports of self-protective behaviour such as avoiding public gatherings or increased sanitation as the basis for further risk analysis.ResultsWe created guidelines for tagging self protective behaviour based on Jones and Salathé (2009)’s behaviour response survey. Applying the guidelines to a corpus of 5283 Twitter messages related to influenza like illness showed a high level of inter-annotator agreement (kappa 0.86). We employed supervised learning using unigrams, bigrams and regular expressions as features with two supervised classifiers (SVM and Naive Bayes) to classify tweets into 4 self-reported protective behaviour categories plus a self-reported diagnosis. In addition to classification performance we report moderately strong Spearman’s Rho correlation by comparing classifier output against WHO/NREVSS laboratory data for A(H1N1) in the USA during the 2009-2010 influenza season.ConclusionsThe study adds to evidence supporting a high degree of correlation between pre-diagnostic social media signals and diagnostic influenza case data, pointing the way towards low cost sensor networks. We believe that the signals we have modelled may be applicable to a wide range of diseases.

Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual's genome 1 by calling genetic variants present in an individual using billions of short, errorful sequence reads 2 . Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome 3,4 .Here we show that a deep convolutional neural network 5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the "highest performance" award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data. Main TextCalling genetic variants from NGS data has proven challenging because NGS reads are not only errorful (with rates from ~0.1-10%) but result from a complex error process that depends on properties of the instrument, preceding data processing tools, and the genome sequence itself 1,3,4,6 . State-of-the-art variant callers use a variety of statistical techniques to model these error processes and thereby accurately identify differences between the reads and the reference genome caused by real genetic variants and those arising from errors in the reads 3,4,6,7 . For example, the widely-used GATK uses logistic regression to model base errors, hidden Markov models to compute read likelihoods, and naive Bayes classification to identify peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/092890 doi: bioRxiv preprint first posted online Dec. 14, 2016; Poplin et al. Creating a universal SNP and small indel variant caller with deep neural networks.variants, which are then filtered to remove likely false positives using a Gaussian mixture model with hand-crafted features capturing common error modes 6 . These techniques allow the GATK to achieve high but still imperfect accuracy on the Illumina sequencing platform 3,4 . Generalizing these models to other sequencing technologies has proven difficult due to the need for manual retuning or exte...

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Sodium dodecyl sulfate (SDS) has been widely shown to strongly promote the formation of methane hydrate. Here we show that SDS displays an extraordinary inhibition effect on methane hydrate formation when the surfactant is used in sub-millimolar concentration (around 0.3 mM). We have also employed Sum Frequency Generation vibrational spectroscopy (SFG) and molecular dynamics simulation (MDS) to elucidate the molecular mechanism of this inhibition. The SFG and MDS results revealed a strong alignment of water molecules underneath surface adsorption of SDS in its sub-millimolar solution. Interestingly, both the alignment of water and the inhibition effect (in 0.3 mM SDS solution) went vanishing when an oppositely-charged surfactant (tetra-n-butylammonium bromide, TBAB) was suitably added to produce a mixed solution of 0.3 mM SDS and 3.6 mM TBAB. Combining structural and kinetic results, we pointed out that the alignment of water underneath surface adsorption of dodecyl sulfate (DS-) anions gave rise to the unexpected inhibition of methane hydration formation in sub-millimolar solution of SDS. The adoption of TBAB mitigated the SDS-induced electrostatic field at the solution's surface and, therefore, weakened the alignment of interfacial water which, in turn, erased the inhibition effect. We discussed this finding using the concept of activation energy of the interfacial formation of gas hydrate. The main finding of this work is to reveal the interplay of interfacial water in governing gas hydrate formation which sheds light on a universal molecular-scale understanding of the influence of surfactants on gas hydrate formation.

In every aspect of human life, sound plays an important role. From personal security to critical surveillance, sound is a key element to develop the automated systems for these fields. Few systems are already in the market, but their efficiency is a point of concern for their implementation in real-life scenarios. The learning capabilities of the deep learning architectures can be used to develop the sound classification systems to overcome efficiency issues of the traditional systems. Our aim, in this paper, is to use the deep learning networks for classifying the environmental sounds based on the generated spectrograms of these sounds. We used the spectrogram images of environmental sounds to train the convolutional neural network (CNN) and the tensor deep stacking network (TDSN). We used two datasets for our experiment: ESC-10 and ESC-50. Both systems were trained on these datasets, and the achieved accuracy was 77% and 49% in CNN and 56% in TDSN trained on the ESC-10. From this experiment, it is concluded that the proposed approach for sound classification using the spectrogram images of sounds can be efficiently used to develop the sound classification and recognition systems. INDEX TERMS Deep learning, convolutional neural network, tensor deep stacking networks, spectrograms.