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Chemosensory protein based olfactory biosensors are expected to play a significant role in next-generation volatile organic compound (VOC) detection systems due to their ultra-high sensitivity and selectivity. As these biosensors can perform most efficiently in aqueous environments, the detection systems need to incorporate a gas sampling interface for gas-to-liquid extraction. This interface should extract the VOCs from the gas phase with high efficiency and transfer them into the liquid containing biosensors to enable subsequent detection. To design such a transfer interface, an understanding of the key parameters influencing the gas-to-liquid extraction efficiency of target VOCs is crucial. This paper reports a gas sampling interface system based on a microfluidic open-channel device for gas-to-liquid extraction. By using this device as a model platform, the key parameters dictating the VOC extraction efficiency were identified. When loaded with 30 μL of capture liquid, the microfluidic device generates a gas-liquid interface area of 3 cm2 without using an interfacial membrane. The pumpless operation based on capillary flow was demonstrated for capture liquid loading and collection. Gas samples spiked with lipophilic model volatiles (hexanal and allyl methyl sulfide) were used for characterization of the VOC extraction efficiency. Decreasing the sampling temperature to 15 °C had a significant impact on increasing capture efficiency, while variation in the gas sampling flow rate had no significant impact in the range between 40–120 mL min−1. This study found more than a 10-fold increase in capture efficiency by chemical modification of the capture liquid with alpha-cyclodextrin. The highest capture efficiency of 30% was demonstrated with gas samples spiked with hexanal to a concentration of 16 ppm (molar proportion). The approach in this study should be useful for further optimisation of miniaturised gas-to-liquid extraction systems and contribute to the design of chemosensory protein-based VOC detection systems.

The investigation of clandestine laboratories poses serious hazards for first responders, emergency services, investigators and the surrounding public due to the risk of exposure to volatile organic compounds (VOCs) used in the manufacture of illicit substances. A novel gas sampling interface using open microfluidic channels that enables the extraction of VOCs out of the gas phase and into a liquid, where it can be analysed by conventional detection systems, has recently been developed. This paper investigates the efficiency and effectiveness of such a gas-to-liquid (GTL) extraction system for the extraction of amphetamine-type substances (ATS) and their precursors from the vapour phase. The GTL interface was evaluated across a range of different ATS and their precursors (methamphetamine, dimethylamphetamine, N-formylmethamphetamine, benzaldehyde, phenyl-2-propanone, ephedrine and pseudoephedrine) at concentrations ranging between 10 and 32 mg m−3. These gas samples were produced by a gas generation system directly in Tedlar® bags and gas canisters for controlled volume sampling. When using gas sampled from Tedlar® bags, four of the seven compounds were able to be extracted by the GTL interface, with the majority of the VOCs having extraction yields between 0.005% and 4.5%, in line with the results from an initial study. When samples were taken from gas canisters, only benzaldehyde was able to be detected, with extraction efficiencies between 0.2% and 0.4%. A custom-built mount for the GTL interface helped to automate the extraction process, with the aim of increasing extraction efficiency or reducing variability. However, the extraction efficiency did not improve when using this accessory, but the procedure did become more efficient. The results from the study indicated that the GTL interface could be employed for the collection of gaseous ATS and incorporated into mobile detection systems for onsite collection and analysis of volatile compounds related to ATS manufacture.

In living tissues, a cell is exposed to chemical substances delivered partially to its surface. Such a heterogeneous chemical environment potentially induces cell polarity. To evaluate this effect, we developed a microfluidic device that realizes spatially confined delivery of chemical substances at subcellular resolution. Our microfluidic device allows simple setup and stable operation for over 4 h to deliver chemicals partially to a single cell. Using the device, we showed that subcellular glucose exposure triggers an intracellular [Ca 2+ ] change in the β-cells. In addition, the imaging of a cell expressing GFP-tagged insulin showed that continuous subcellular exposure to glucose biased the spatial distribution of insulin granules toward the site where the glucose was delivered. Our approach illustrates an experimental technique that will be applicable to many biological experiments for imaging the response to subcellular chemical exposure and will also provide new insights about the development of polarity of β-cells.

ABSTRACT Many motile bacteria are propelled by the rotation of flagellar filaments. This rotation is driven by a membrane protein known as the stator-complex, which drives the rotor of the bacterial flagellar motor. Torque generation is powered in most cases by proton transit through membrane protein complexes known as stators, with the next most common ionic power source being sodium. Sodium-powered stators can be studied through the use synthetic chimeric stators that combine parts of sodium- and proton-powered stator proteins. The most well studied example is the use of the sodium powered PomA-PotB chimeric stator unit in the naturally proton-powered E. coli. Here we designed a fluidics system at low cost for rapid prototyping to separate motile and non-motile populations of bacteria while varying the ionic composition of the media and thus the sodium-motive-force available to drive this chimeric flagellar motor. We measured separation efficiencies at varying ionic concentrations and confirmed using fluorescence that our device delivered eight-fold enrichment of the motile proportion of a mixed population. Furthermore, our results showed that we could select bacteria from reservoirs where sodium was not initially present. Overall, this technique can be used to implement selection of highly-motile fractions from mixed liquid cultures, with applications in directed evolution to investigate the adaptation of motility in bacterial ecosystems. Competing Interest Statement The authors have declared no competing interest. Footnotes * Updated text for introduction, results and discussion to discuss further existing literature for separation of bacteria and the specific novelty of application on sodium-powered chimera prior to resubmission.

Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. Les cryptomonnaies et la technologie sous-jacente de chaînes de blocs ont récemment retenu l’attention publique. Contrairement aux monnaies fiduciaires, les transactions de cryptomonnaie telles que le Bitcoin et le Litecoin sont enregistrées à perpétuité dans un grand livre distribué visible publiquement. Les auteurs profitent de cette visibilité publique afin de construire un réseau complexe des interactions financières qui permet d’étudier le graphe des chaînes de blocs, mais également la relation entre plusieurs caractéristiques des réseaux de chaînes de blocs et les risques d’investissements en cryptomonnaie. Ils proposent le concept novateur de chaînettes, ou motifs de chaînes de blocs, afin d’exploiter cette information. Ils utilisent les chaînettes afin d’évaluer la topologie locale des structures de chaînes de blocs sur la formation des prix de Bitcoin et de Litecoin et leur dynamique. Ils étudient la causalité de Granger des chaînettes pour la prévision et identifient certains types de chaînettes qui exhibent la plus forte influence prédictive sur le prix des cryptomonnaies et leur risque. De façon générale, même si de nombreux aspects de ce type de données demeurent inexplorés, les auteurs mettent en lumière divers défis théoriques, méthodologiques et appliqués de l’analyse de données de chaînes de blocs qui sauront éveiller l’intérêt de la communauté statistique.

We consider the problem of evaluating the quality of solution sets generated by heuristics for multiple-objective combinatorial optimization problems. We extend previous research on the integrated preference functional (IPF), which assigns a scalar value to a given discrete set of nondominated points so that the weighted Tchebycheff function can be used as the underlying implicit value function. This extension is useful because modeling the decision maker's value function with the weighted Tchebycheff function reflects the impact of unsupported points when evaluating sets of nondominated points. We present an exact calculation method for the IPF measure in this case for an arbitrary number of criteria. We show that every nondominated point has its optimal weight interval for the weighted Tchebycheff function. Accordingly, all nondominated points, and not only the supported points in a set, contribute to the value of the IPF measure when using the weighted Tchebycheff function. Two- and three-criteria numerical examples illustrate the desirable properties of the weighted Tchebycheff function, providing a richer measure than the original IPF based on a convex combination of objectives.

Bitcoin and its underlying technology, blockchain, have gained significant popularity in recent years. Satoshi Nakamoto designed Bitcoin to enable a secure, distributed platform without the need for central authorities, and blockchain has been hailed as a paradigm that will be as impactful as Big Data, Cloud Computing, and Machine Learning. Blockchain incorporates innovative ideas from various fields, such as public-key encryption and distributed systems. As a result, readers often encounter resources that explain Blockchain technology from a single perspective, leaving them with more questions than answers. In this primer, we aim to provide a comprehensive view of blockchain. We will begin with a brief history and introduce the building blocks of the blockchain. As graph mining is a major area of blockchain analysis, we will delve into the graph-theoretical aspects of Blockchain technology. We will also discuss the future of blockchain and explain how extensions such as smart contracts and decentralized autonomous organizations will function. Our goal is to provide a concise but complete description of blockchain technology that is accessible to readers with no prior expertise in the field.

Topological data analysis (TDA) delivers invaluable and complementary information on the intrinsic properties of data inaccessible to conventional methods. However, high computational costs remain the primary roadblock hindering the successful application of TDA in real-world studies, particularly with machine learning on large complex networks. Indeed, most modern networks such as citation, blockchain, and online social networks often have hundreds of thousands of vertices, making the application of existing TDA methods infeasible. We develop two new, remarkably simple but effective algorithms to compute the exact persistence diagrams of large graphs to address this major TDA limitation. First, we prove that \\((k+1)\\)-core of a graph \\(\\mathcal{G}\\) suffices to compute its \\(k^{th}\\) persistence diagram, \\(PD_k(\\mathcal{G})\\). Second, we introduce a pruning algorithm for graphs to compute their persistence diagrams by removing the dominated vertices. Our experiments on large networks show that our novel approach can achieve computational gains up to 95%. The developed framework provides the first bridge between the graph theory and TDA, with applications in machine learning of large complex networks. Our implementation is available at https://github.com/cakcora/PersistentHomologyWithCoralPrunit

Blockchain is an emerging technology that has enabled many applications, from cryptocurrencies to digital asset management and supply chains. Due to this surge of popularity, analyzing the data stored on blockchains poses a new critical challenge in data science. To assist data scientists in various analytic tasks on a blockchain, in this tutorial, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. We discuss how we can abstract blockchain data as various types of networks and further use such associated network abstractions to reap important insights on blockchains' structure, organization, and functionality.

Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. At a high level, our defense is based on carefully adjusting the aggregation server's learning rate, per dimension and per round, based on the sign information of agents' updates. We first conjecture the necessary steps to carry a successful backdoor attack in FL setting, and then, explicitly formulate the defense based on our conjecture. Through experiments, we provide empirical evidence that supports our conjecture, and we test our defense against backdoor attacks under different settings. We observe that either backdoor is completely eliminated, or its accuracy is significantly reduced. Overall, our experiments suggest that our defense significantly outperforms some of the recently proposed defenses in the literature. We achieve this by having minimal influence over the accuracy of the trained models. In addition, we also provide convergence rate analysis for our proposed scheme.