Flossing DNA in a Dual Nanopore Device

Solid‐state nanopores are a single‐molecule technique that can provide access to biomolecular information that is otherwise masked by ensemble averaging. A promising application uses pores and barcoding chemistries to map molecular motifs along single DNA molecules. Despite recent research breakthroughs, however, it remains challenging to overcome molecular noise to fully exploit single‐molecule data. Here, an active control technique termed “flossing” that uses a dual nanopore device is presented to trap a proteintagged DNA molecule and up to 100's of back‐and‐forth electrical scans of the molecule are performed in a few seconds. The protein motifs bound to 48.5 kb λ‐DNA are used as detectable features for active triggering of the bidirectional control. Molecular noise is suppressed by averaging the multiscan data to produce averaged intertag distance estimates that are comparable to their known values. Since nanopore feature‐mapping applications require DNA linearization when passing through the pore, a key advantage of flossing is that trans‐pore linearization is increased to >98% by the second scan, compared to 35% for single nanopore passage of the same set of molecules. In concert with barcoding methods, the dual‐pore flossing technique could enable genome mapping and structural variation applications, or mapping loci of epigenetic relevance.     

EEG source localization using a sparsity prior based on Brodmann areas

Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.     

Enhancement of oral bioavailability of anti-HIV drug rilpivirine HCl through nanosponge formulation*

Objective: To synthesize β cyclodextrin nanosponges using a novel and efficient microwave mediated method for enhancing bioavailability of Rilpivirine HCl (RLP).

Significance: Belonging to BCS class II RLP has pH dependent solubility and poor oral bioavailability. However, a fatty meal enhances its absorption hence the therapy indicates that the dosage form be consumed with a meal. But then it becomes tedious and inconvenient to continue the therapy for years with having to face the associated gastric side effects such as nausea.

Method: Microwave synthesizer was used to mediate the poly-condensation reaction between β-cyclodextrin and cross-linker diphenylcarbonate. Critical parameters selected were polymer to cross-linker ratio, Watt power, reaction time and solvent volume. Characterization studies were performed using FTIR, DSC, SEM, ¹H-NMR and PXRD. Molecular modeling was applied to confirm the possibility of drug entrapment. In vitro drug dissolution followed by oral bioavailability studies was performed in Sprawley rats. Samples were analyzed using HPLC.

Results: Microwave synthesis yields para-crystalline, porous nanosponges (～205?nm). Drug entrapment led to enhancement of solubility and a two-fold increase in drug dissolution (P?C_max and AUC_0-∞ increases significantly (C_max of NS～ 586?±?5.91?ng/mL; plain RLP ～310?±?5. 74?ng/mL).

Conclusion: The approach offers a comfortable dosing zone for AIDs patients, negating the requirement of consuming the formulation in a fed state due to enhancement in drugs’ oral bioavailability.     

Ion implantation effects of negative oxygen on copper nanowires

Copper nanowires of diameter 80 nm were synthesized in polycarbonate membrane using template technique. Samples were then implanted with 160 keV O^?1 ion beam with varying particle fluence of 1?×?10¹², 5?×?10¹² and 1?×?10¹³ ions/cm². The SRIM (Stopping and range of ions in matter) software was used to study the processes involved. Compositional analysis confirms implantation of oxygen ions and the stoichiometry of Cu:O was found to be 6:1 by weight % when implanted at 1?×?10¹³ ions/cm². Scanning electron microscopy reveals no changes in morphology of nanowires on implantation. X-ray diffraction analysis showed no shifting in the ‘2θ’ position of diffraction peaks however some new diffraction peaks of oxygen were seen. Implantation with oxygen ion led to the increased crystallite size and reduced strain. The conductivity of the nanowires was found to increase linearly with the ion fluence presenting constructive effect of negative ion implantation on copper nanowires.     

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users’ emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’ emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.     

Stability Analysis of Epidemic Modeling Based on Spatial Correlation for Wireless Sensor Networks

Wireless Personal Communications - In wireless sensor networks (WSNs), the main task of each sensor node is to sense the physical activity and then report it to a remote central monitoring sink...     

The Impact of Social Media on Consumers’ Acculturation and Purchase Intentions

Social media has emerged as a significant and effective means of assisting and endorsing activities and communications among peers, consumers and organizations that outdo the restrictions of time and space. While the previous studies acknowledge the role of agents of culture change, it largely remains silent on the role of social media in influencing acculturation outcomes and consumption choices. This study uses self-administered questionnaire to collect data from 514 Turkish-Dutch respondents and examines how their use of social media affects their acculturation and consumption choices. This research makes a significant contribution to consumer acculturation research by showing that social media is a vital means of culture change and a driver of acculturation strategies and consumption choices. This study is the first to investigate the role of social media as an agent of culture change in terms of how it impacts acculturation and consumption. The paper discusses implications for theory development and for practice.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

Advanced Bioresponsive Multitasking Hydrogels in the New Era of Biomedicine

Advanced forms of hydrogels have many inherently desirable properties and can be designed with different structures and functions. In particular, bioresponsive multifunctional hydrogels can carry out sophisticated biological functions. These include in situ single-cell approaches, capturing, analysis, and release of living cells, biomimetics of cell, tissue, and tumor-specific niches. They can allow in vivo cell manipulation and act as novel drug delivery systems, allowing diagnostic, therapeutic, vaccination, and immunotherapy methods. In the present review of multitasking hydrogels, new approaches and devices classified into point-of-care testing (POCT), microarrays, single-cell/rare cell approaches, artificial membranes, biomimetic modeling systems, nanodoctors, and microneedle patches are summarized. The potentials and application of each format are critically discussed, and some limitations are highlighted. Finally, how hydrogels can enable an “all-in-one platform” to play a key role in cancer therapy, regenerative medicine, and the treatment of inflammatory, degenerative, genetic, and metabolic diseases is being looked forward to.