Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.

     

Potential role of root membrane phosphatidic acid in superior agronomic performance of silage‐corn cultivated in cool climate cropping systems

The literature is replete with information describing the composition of the root lipidome in several plant species grown under various environmental conditions. However, it is unknown to what extent the root membrane lipidome vary between silage‐corn genotypes, and how such variation could influence agronomic performances during field cultivation in cool climate. To address this issue, the root membrane lipidome and agronomic performance were assessed for five silage‐corn genotypes (Fusion‐RR, Yukon‐R, A4177G3‐RIB, DKC23‐17RIB, DKC26‐28RIB) cultivated under cool climatic conditions. Leaf area, plant height and biomass production were used as agronomic performance indicators. Varieties DKC26‐28RIB and Yukon‐R expressed significantly higher leaf area, plant height and biomass production compared to the other genotypes. A strong positive Spearman rank‐order correlation (P = 0.001) was observed between biomass production and root phosphatidic acid (PA). The high correlation observed between PA and agronomic performance indicates PA could potentially be used as biomarker to assist in the selection of silage‐corn genotypes with superior agronomic performance ideally suited for field cultivations in cool climatic conditions.     

Involvement of the autophagy pathway in trafficking of Mycobacterium tuberculosis bacilli through cultured human type II epithelial cells

Interactions between Mycobacterium tuberculosis bacilli and alveolar macrophages have been extensively characterized, while similar analyses in epithelial cells have not been performed. In this study, we microscopically examined endosomal trafficking of M. tuberculosis strain Erdman in A549 cells, a human type II pneumocyte cell line. Immuno‐electron microscopic (IEM) analyses indicate that M. tuberculosis bacilli are internalized to a compartment labelled first with Rab5 and then with Rab7 small GTPase proteins. This suggests that, unlike macrophages, M. tuberculosis bacilli traffic to late endosomes in epithelial cells. However, fusion of lysosomes with the bacteria‐containing compartment appears to be inhibited, as illustrated by IEM studies employing LAMP‐2 and cathepsin‐L antibodies. Examination by transmission electron microscopy and IEM revealed M. tuberculosis‐containing compartments surrounded by double membranes and labelled with antibodies against the autophagy marker Lc3, providing evidence for involvement and intersection of the autophagy and endosomal pathways. Interestingly, inhibition of the autophagy pathway using 3‐methyladenine improved host cell viability and decreased numbers of viable intracellular bacteria recovered after 72 h post infection. Collectively, these datasuggest that trafficking patterns for M. tuberculosis bacilli in alveolar epithelial cells differ from macrophages, and that autophagy is involved this process.     

The Moraxella adhesin UspA1 binds to its human CEACAM1 receptor by a deformable trimeric coiled-coil

Moraxella catarrhalis is a ubiquitous human-specific bacterium commonly associated with upper and lower respiratory tract infections, including otitis media, sinusitis and chronic obstructive pulmonary disease. The bacterium uses an autotransporter protein UspA1 to target an important human cellular receptor carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1). Using X-ray crystallography, we show that the CEACAM1 receptor-binding region of UspA1 unusually consists of an extended, rod-like left-handed trimeric coiled-coil. Mutagenesis and binding studies of UspA1 and the N-domain of CEACAM1 have been used to delineate the interacting surfaces between ligand and receptor and guide assembly of the complex. However, solution scattering, molecular modelling and electron microscopy analyses all indicate that significant bending of the UspA1 coiled-coil stalk also occurs. This explains how UspA1 can engage CEACAM1 at a site far distant from its head group, permitting closer proximity of the respective cell surfaces during infection.     

Taxonomic implications of foliar epidermis in Impatiens (Balsaminaceae): Investigating 12 Pakistani taxa as an example

Impatiens L. is taxonomically a complicated genus that necessitates fresh characterization to resolve its taxonomy. This study therefore aimed at ascertaining micro-morphological characters in the foliar epidermis of Impatiens. For this purpose, we gathered twelve taxa (10 species and 2 subspecies) mostly from the northern regions of Pakistan and studied qualitative and quantitative characters depicting variation in epidermal cells, stomata, and trichomes on both surfaces. Multicellular, uniseriate trichomes were observed only in I. balsamina L., and raphides in I. balfourii Hook. f. and I. thomsonii Hook. f. while stomata were exclusively anomocytic in all species. Morphological variation in the foliar epidermal characters and their diagnostic value has been depicted by constructing a taxonomic key to all taxa. The study revealed that although foliar epidermal characters in Impatiens did not desirably assort taxa on the basis of structural topographies, yet demonstrated adequate variation distinguishing the Pakistani taxa.     

An EEG-based machine learning method to screen alcohol use disorder

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.     

Severity-Based Adaptation with Limited Data for ASR to Aid Dysarthric Speakers

Automatic speech recognition (ASR) is currently used in many assistive technologies, such as helping individuals with speech impairment in their communication ability. One challenge in ASR for speech-impaired individuals is the difficulty in obtaining a good speech database of impaired speakers for building an effective speech acoustic model. Because there are very few existing databases of impaired speech, which are also limited in size, the obvious solution to build a speech acoustic model of impaired speech is by employing adaptation techniques. However, issues that have not been addressed in existing studies in the area of adaptation for speech impairment are as follows: (1) identifying the most effective adaptation technique for impaired speech; and (2) the use of suitable source models to build an effective impaired-speech acoustic model. This research investigates the above-mentioned two issues on dysarthria, a type of speech impairment affecting millions of people. We applied both unimpaired and impaired speech as the source model with well-known adaptation techniques like the maximum likelihood linear regression (MLLR) and the constrained-MLLR(C-MLLR). The recognition accuracy of each impaired speech acoustic model is measured in terms of word error rate (WER), with further assessments, including phoneme insertion, substitution and deletion rates. Unimpaired speech when combined with limited high-quality speech-impaired data improves performance of ASR systems in recognising severely impaired dysarthric speech. The C-MLLR adaptation technique was also found to be better than MLLR in recognising mildly and moderately impaired speech based on the statistical analysis of the WER. It was found that phoneme substitution was the biggest contributing factor in WER in dysarthric speech for all levels of severity. The results show that the speech acoustic models derived from suitable adaptation techniques improve the performance of ASR systems in recognising impaired speech with limited adaptation data.