2-Aminobenzothiazoles Inhibit Virulence Gene Expression and Block Polymyxin Resistance in Salmonella enterica

One promising strategy to combat antibiotic-resistant bacteria is to develop compounds that block bacterial defenses against antibacterial conditions produced by the innate immune system. Salmonella enterica, which causes food-borne gastroenteritis and typhoid fever, requires histidine kinases (HKs) to resist innate immune defenses such as cationic antimicrobial peptides (CAMPs). Herein, we report that 2-aminobenzothiazoles block histidine kinase-dependent phenotypes in Salmonella enterica serotype Typhimurium. We found that 2-aminobenzothiazoles inhibited growth under low Mg²⁺, a stressful condition that requires histidine kinase-mediated responses, and decreased expression of the virulence genes pagC and pagK. Furthermore, we discovered that 2-aminobenzothiazoles weaken Salmonella’s resistance to polymyxin B and polymyxin E, which are last-line antibiotics and models for host defense CAMPs. These findings raise the possibilities that 2-aminobenzothiazoles can block HK-mediated bacterial defenses and can be used in combination with polymyxins to treat infections caused by Salmonella.     

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

Long‐Term Electrical and Mechanical Function Monitoring of a Human‐on‐a‐Chip System

The goal of human‐on‐a‐chip systems is to capture multiorgan complexity and predict the human response to compounds within physiologically relevant platforms. The generation and characterization of such systems is currently a focal point of research given the long‐standing inadequacies of conventional techniques for predicting human outcome. Functional systems can measure and quantify key cellular mechanisms that correlate with the physiological status of a tissue, and can be used to evaluate therapeutic challenges utilizing many of the same endpoints used in animal experiments or clinical trials. Culturing multiple organ compartments in a platform creates a more physiologic environment (organ–organ communication). Here is reported a human 4‐organ system composed of heart, liver, skeletal muscle, and nervous system modules that maintains cellular viability and function over 28 days in serum‐free conditions using a pumpless system. The integration of noninvasive electrical evaluation of neurons and cardiac cells and mechanical determination of cardiac and skeletal muscle contraction allows the monitoring of cellular function, especially for chronic toxicity studies in vitro. The 28‐day period is the minimum timeframe for animal studies to evaluate repeat dose toxicity. This technology can be a relevant alternative to animal testing by monitoring multiorgan function upon long‐term chemical exposure.     

Waterproof,Breathable, and Antibacterial Self‐Powered e‐Textiles Based on Omniphobic Triboelectric Nanogenerators

Multifunctional electronic textiles (e‐textiles) incorporating miniaturized electronic devices will pave the way toward a new generation of wearable devices and human–machine interfaces. Unfortunately, the development of e‐textiles is subject to critical challenges, such as battery dependence, breathability, satisfactory washability, and compatibility with mass production techniques. This work describes a simple and cost‐effective method to transform conventional garments and textiles into waterproof, breathable, and antibacterial e‐textiles for self‐powered human–machine interfacing. Combining embroidery with the spray‐based deposition of fluoroalkylated organosilanes and highly networked nanoflakes, omniphobic triboelectric nanogenerators (R^F‐TENGs) can be incorporated into any fiber‐based textile to power wearable devices using energy harvested from human motion. R^F‐TENGs are thin, flexible, breathable (air permeability 90.5 mm s^?1), inexpensive to fabricate (<0.04$ cm^?2), and capable of producing a high power density (600 µW cm^?2). E‐textiles based on R^F‐TENGs repel water, stains, and bacterial growth, and show excellent stability under mechanical deformations and remarkable washing durability under standard machine‐washing tests. Moreover, e‐textiles based on R^F‐TENGs are compatible with large‐scale production processes and exhibit high sensitivity to touch, enabling the cost‐effective manufacturing of wearable human–machine interfaces.     

Touch‐Interactive Flexible Sustainable Energy Harvester and Self‐Powered Smart Card

Sustainable and safe energy sources combined with cost effectiveness are major goals for society when considering the current scenario of mass production of portable and Internet of Things (IoT) devices along with the huge amount of inevitable e‐waste. The conceptual design of a self‐powered “eco‐energy” smart card based on paper promotes green and clean energy, which will bring the zero e‐waste challenge one step closer to fruition. A commercial raw filter paper is modified through a fast in situ functionalization method, resulting in a conductive cellulose fiber/polyaniline composite, which is then applied as an energy harvester based on a mechano‐responsive charge transfer mechanism through a metal/conducting polymer interface. Different electrodes are studied to optimize charge transfer based on contact energy level differences. The highest power density and current density obtained from such a paper‐based “eco‐energy” smart card device are 1.75 W m^?2 and 33.5 mA m^?2 respectively. This self‐powered smart energy card is also able to light up several commercial light‐emitting diodes, power on electronic devices, and charge capacitors.     

Modeling breakage and motion of black pepper seeds in cryogenic mill

The present investigation aims to represent three-dimensional motion and breakage phenomena of black pepper seeds in the cryogenic mill (hammer mill) using discrete element method (DEM). In DEM modeling, bonded particle model was coupled with Hertz-Mindlin contact model. Calibration method was used to select appropriate model (bond) parameters. The calibrated set of bond parameters includes 3.12?×?10¹¹?Pa?m^?1 normal stiffness; 1.56?×?10¹¹?Pa?m^?1 shear stiffness; 3.88?×?10⁸?Pa critical normal stiffness; 1.94?×?10⁸?Pa critical shear stiffness. Besides, the validity of calibrated parameters was tested in the hammer mill. The observed qualitative and quantitative results (breakage and flow pattern) of numerical and experimental approaches were in good agreement. Based on these results, a few prefatory suggestions were provided to improve the design aspects of the mill. Overall, DEM modeling offered a better understanding of particle breakage and flow pattern in the mill.     

Laser-Induced Graphene for Electrothermally Controlled,Mechanically Guided, 3D Assembly and Human–Soft Actuators Interaction

Mechanically guided, 3D assembly has attracted broad interests, owing to its compatibility with planar fabrication techniques and applicability to a diversity of geometries and length scales. Its further development requires the capability of on-demand reversible shape reconfigurations, desirable for many emerging applications (e.g., responsive metamaterials, soft robotics). Here, the design, fabrication, and modeling of soft electrothermal actuators based on laser-induced graphene (LIG) are reported and their applications in mechanically guided 3D assembly and human–soft actuators interaction are explored. Over 20 complex 3D architectures are fabricated, including reconfigurable structures that can reshape among three distinct geometries. Also, the structures capable of maintaining 3D shapes at room temperature without the need for any actuation are realized by fabricating LIG actuators at an elevated temperature. Finite element analysis can quantitatively capture key aspects that govern electrothermally controlled shape transformations, thereby providing a reliable tool for rapid design optimization. Furthermore, their applications are explored in human–soft actuators interaction, including elastic metamaterials with human gesture-controlled bandgap behaviors and soft robotic fingers which can measure electrocardiogram from humans in an on-demand fashion. Other demonstrations include artificial muscles, which can lift masses that are about 110 times of their weights and biomimetic frog tongues which can prey insects.