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As it has become increasingly common for social network users to write and view post in languages other than English, most social networks now provide machine translations to allow posts to be read by an audience beyond native speakers. However, authors typically cannot view the translations of their posts and have little control over these translations. To address this issue, I am developing a prototype that will provide authors with transparency of and more personalized control over the translation of their posts.

Funder: Singapore Ministry of Health's National Medical Research Council, Funder: National Institute for Health Research, Funder: Wellcome Trust or the Department of Health, Funder: NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme; Grant(s): IS-BRC-1215-20014

Background and aimsThis paper describes the data resource profile of South Asia Biobank (SAB), which was set up in South Asia from November 2018 to March 2020, to identify the risk factors and their complex interactions underlying the development of type-2 diabetes mellitus, cardiovascular disease and other chronic diseases in South Asians.Data resource basicsThis cross-sectional population-based study has recruited 52713 South Asian adults from 118 surveillance sites at five centres of excellence in South Asia (Bangladesh, North India, South India, Pakistan and Sri Lanka). Structured assessments of participants included six complementary domains: i). Registration and consent; ii). Questionnaire (information on behavioural risk factors, personal and family medical history, medications, socioeconomic status); iii). Physical measurements (height, weight, waist and hip circumference and bio-impedance for body fat composition, blood pressure, cardiac evaluation, retinal photography, respiratory evaluation); iv). Biological samples (blood and urine); v). Physical activity monitoring and vi). Dietary intake by a 24-hour recall. Aliquots of whole blood, serum, plasma, and urine were put into storage at −80°C for further analysis.Key resultsThe prevalence of obesity is 6.6% in Bangladesh, 19.7% in India, 33.9% in Pakistan and 15.7% in Sri Lanka. The prevalence of diabetes is 11.5%, 27.7%, 25.3%, and 24.8%, and the prevalence of hypertension is 26.7%, 36.9%, 44.5%, 35.0% in Bangladesh, India, Pakistan and Sri Lanka respectively.Collaboration and data accessSAB is the first comprehensive biobank of South Asian individuals. Collected data are available to the global scientific community upon request.

LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an $F_{\mathrm{ score}}$ of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency.

Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neural networks is proposed to identify impacted areas and accessible roads in post-disaster scenarios. The effectiveness of pretraining with ImageNet on the task of aerial image segmentation has been analysed and performances of popular segmentation models compared. Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models. Open data available from OpenStreetMap (OSM) is used for training, forgoing the need for time-consuming manual annotation. The method also makes use of graph theory to update road network data available from OSM and to detect the changes caused by a natural disaster. Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer parameters compared to ENet, achieved comparable segmentation results to that of the state-of-the-art networks., Comment: Accepted in Neurocomputing, 2020

Explainability is an important factor to drive user trust in the use of neural networks for tasks with material impact. However, most of the work done in this area focuses on image analysis and does not take into account 3D data. We extend the saliency methods that have been shown to work on image data to deal with 3D data. We analyse the features in point clouds and voxel spaces and show that edges and corners in 3D data are deemed as important features while planar surfaces are deemed less important. The approach is model-agnostic and can provide useful information about learnt features. Driven by the insight that 3D data is inherently sparse, we visualise the features learnt by a voxel-based classification network and show that these features are also sparse and can be pruned relatively easily, leading to more efficient neural networks. Our results show that the Voxception-ResNet model can be pruned down to 5\% of its parameters with negligible loss in accuracy., Comment: Accepted for IJCNN 2020

Anemia is a condition that affects over 1.6 billion people worldwide untreated, the disease could lead to increased morbidity and mortality during pregnancy, affecting both the mother and the unborn child. This paper presents the measured dielectric properties of whole blood samples from 176 patients obtained from a hematology clinic; with 80 samples from male patients and 96 samples from female patients. Measurements were performed using a Keysight slim form probe and Keysight network analyzer to obtain the dielectric properties over a wide frequency range (500 MHz-8.5 GHz). A multiple linear regression analysis is performed to identify which components of the blood show the highest correlation with changes in the dielectric properties. Hemoglobin (Hgb) is shown to be the biggest predictor of changes in complex permittivity, demonstrating that permittivity measurements at a single frequency can potentially be used to detect anemia. A support vector machines algorithm is trained and tested to classify between blood samples from healthy patients and blood samples from patients with anemia. The classifier is optimized using a Bayesian-optimization approach during 10-fold cross-validation and then the average performance of the final trained classifier is evaluated through 10-fold testing on unseen data sets. Using a clinical definition of anemia defined as patients having a concentration of Hgb

The lungs are the most vulnerable site for air-borne infections. Immunologic compartmentalization of the lungs into airway lumen and interstitium has paved the way to determine the immune status of the site of pathogen entry, which is crucial for the outcome of any air-borne infections. Vaccination via the nasal route with Mycobacterium indicus pranii (MIP), a prospective candidate vaccine against tuberculosis (TB), has been reported to confer superior protection as compared to the subcutaneous (s.c.) route in small-animal models of TB. However, the immune mechanism remains only partly understood. Here, we showed that intranasal (i.n.) immunization of mice with MIP resulted in a significant recruitment of CD4+ and CD8+ T-cells expressing activation markers in the lung airway lumen. A strong memory T-cell response was observed in the lung airway lumen after i.n. MIP vaccination, compared with s.c. vaccination. The recruitment of these T-cells was regulated primarily by CXCR3–CXCL11 axis in “MIP i.n.” group. MIP-primed T-cells in the lung airway lumen effectively transferred protective immunity into naïve mice against Mycobacterium tuberculosis (M.tb) infection and helped reducing the pulmonary bacterial burden. These signatures of protective immune response were virtually absent or very low in unimmunized and subcutaneously immunized mice, respectively, before and after M.tb challenge. Our study provides mechanistic insights for MIP-elicited protective response against M.tb infection.

Accurate road maps are important for timely disaster relief efforts and risk management. Current disaster mapping is done manually by volunteers following a disaster and the process is slow and error prone. We propose a framework for identifying accessible roads in post-disaster satellite imagery by detecting changes from pre-disaster imagery, in conjunction with OpenStreetMap data. We validate our results with data from Indonesia 2018 tsunami, obtained from DigitalGlobe.

E-Banking has become an integral part of the global financial environment. Improvement in technologies and financial innovations has made E-banking is an intense part of the banking sector. “As day by day increasing the technologies are also increasing”. Technology has become the fuel for rapid change. In earlier times, the banking customers were required to visit in a bank in order to transact their accounts in the bank but now by the help of E-Banking the customers do not need to visit in a bank and with the help of internet, customers can easily transact their accounts from anywhere. E- Banking is playing a major role that it’s improving the service quality and strengthens the banking sector because of the electronic payment there is increase in customer satisfaction level, increased productivity, reduction in cost of banking operations, settlement faster and in large volumes. The world has become a global village and it has brought a revolution in the banking industry because of increasing in the development of information technology. The key trends are discussed for their impact on future E-Banking services.

Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting changes from pre-disaster imagery. Graph theory is combined with the CNN output for detecting semantic changes in road networks with OpenStreetMap data. Our results are validated with data of a tsunami-affected region in Palu, Indonesia acquired from DigitalGlobe.

The detection of buried minimum-metal anti-personnel landmines is a time-consuming problem, due to the high false alarm rate (FAR) arising from metallic clutter typically found in minefields. Magnetic induction spectroscopy (MIS) offers a potential way to reduce the FAR by classifying the metallic objects into threat and non-threat categories, based on their spectroscopic signatures. A new algorithm for threat identification for MIS sensors, based on a fully-connected artificial neural network (ANN), is proposed in this paper, and compared against a classifier based on Support Vector Machines (SVM). The results demonstrate that MIS is a potentially viable option for the reduction of false alarms in humanitarian demining. It is also shown that the ANN outperforms the SVM-based approach for threat objects containing minimal amounts of metal.

In medical field, ultrasound imaging technique is a major medium for the doctors and radiologists to diagnose the patient’s problem. The presence of speckle noise degrades the visuals of the ultrasound image and making it difficult for the doctors to diagnose properly. Hence, denoising and restoration of these images came up as a challenge to enhance the quality of the image. In this paper, an improved non-local mean filter is proposed for suppression of speckle noise in ultrasound image. This improved filter is the combination of two conventional filters, i.e., non-local mean filter and bilateral filter. The newly modified method which is emerged by combining the functions of both the conventional filters results in the improvement of quality of image. The three image quality assessment parameters are being used to analyze the performance of improved non-local mean filter, i.e., peak signal-to-noise-ratio (PSNR) and structural similarity (SSIM).

A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.

BCG, the only approved vaccine protects against severe form of childhood tuberculosis but its protective efficacy wanes in adolescence. BCG has reduced the incidence of infant TB considerably in endemic areas; therefore prime-boost strategy is the most realistic measure for control of tuberculosis in near future. Mycobacterium indicus pranii (MIP) shares significant antigenic repertoire with Mtb and BCG and has been shown to impart significant protection in animal models of tuberculosis. In this study, MIP was given as a booster to BCG vaccine which enhanced the BCG mediated immune response, resulting in higher protection. MIP booster via aerosol route was found to be more effective in protection than subcutaneous route of booster immunization. Pro-inflammatory cytokines like IFN-γ, IL-12 and IL-17 were induced at higher level in infected lungs of 'BCG-MIP' group both at mRNA expression level and in secretory form when compared with 'only BCG' group. BCG-MIP groups had increased frequency of multifunctional T cells with high MFI for IFN-γ and TNF-α in Mtb infected mice. Our data demonstrate for the first time, potential application of MIP as a booster to BCG vaccine for efficient protection against tuberculosis. This could be very cost effective strategy for efficient control of tuberculosis.

The suppression of speckle noise is necessary for clear vision of ultrasound images. The quality of ultrasound images is degraded by the presence of speckle noise. In this work, bilateral Filter is used to suppress speckle noise. Conventionally, this filter is used to suppress the Gaussian noise from the images. A bilateral filter is better at edge preserving, noise suppression and for better smoothening of gray as well as colored images. Bilateral filter tends to improve image quality as it replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. These weights are basically based on Gaussian distribution function. The three parameters have been used to analyze the performance of bilateral filter the are PSNR, SSIM, and SSI.

The speckle suppression is very important for carrying out proper clinical operation in ultrasound images. Speckle is a kind of multiplicative noise which exists inherently within the ultrasound images. Although there are many speckle noise suppression filters from the ultrasound images, it possesses certain constraints. In this paper, the Non-local Mean (NLM) filter is used to suppress speckle noise from ultrasound images. NLM filter consists of two windows that are search and similarity windows which can improve upon the limitation of many conventional filters. The simulation results of NLM filter for the values of different noise variances on ultrasound image are shown. Finally, the performance analysis is done by image quality assessment parameters like Peak Signal-to-Noise Ratio (PSNR) and Coefficient of Correlation (CoC).

// Muhammad Mosaraf Hossain 1 , David Barua 1 , Vahid Arabkari 1 , Nahidul Islam 2 , Ananya Gupta 3 and Sanjeev Gupta 1 1 Discipline of Pathology, School of Medicine, Lambe Institute for Translational Research, National University of Ireland Galway, Galway, Ireland 2 Regenerative Medicine Institute (REMEDI) at CURAM Centre for Research in Medical Devices, School of Medicine, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland 3 Discipline of Physiology, School of Medicine, National University of Ireland Galway, Galway, Ireland Correspondence to: Sanjeev Gupta, email: sanjeev.gupta@nuigalway.ie Keywords: steroid receptor coactivators; unfolded protein response; breast cancer; PERK; NCOA3 Received: November 03, 2017 Accepted: February 01, 2018 Published: February 08, 2018 ABSTRACT Nuclear receptor coactivators (NCOAs) function as coactivators for nuclear receptors as well as several other transcription factors and potentiate their transcriptional activity. NCOAs play an important role in biology of hormone-dependent and -independent cancers. MCB-613 is a recently described, small molecule stimulator of NCOAs and anti-neoplastic compound that leads to the death of tumour cells due to increased cellular stress. In the present study we investigated the molecular mechanism of MCB-613-induced cell death. We report that absence of NCOA3 leads to compromised activation of PERK signalling pathway during unfolded protein response (UPR). We found that chemical and genetic inhibition of NCOA3 attenuated the expression of PERK at mRNA and protein level. We show that loss of NCOA3 renders cells hypersensitive to UPR induced cell death. Our results show that MCB-613 induced cell death is attenuated in NCOA3 knockout HeLa cells and MCB-613 leads to enhanced PERK signalling in wild-type HeLa cells. The knockdown of PERK provides resistance to MCB-613 mediated cell death while knockdown of XBP1 and ATF6 have no such effect. Our results suggest that hyperstimulation of NCOA3 by MCB-613 induces cell death by evoking constitutive PERK signalling. Taken together our results point to NCOA3 as an important determinant in regulating cell fate during ER stress, with too little and too much NCOA3 both producing deleterious effects.

Sorafenib is a multikinase inhibitor that acts by inhibiting tumour growth and disrupting tumour microvasculature through anti-proliferative, anti-angiogenic, and pro-apoptotic effects. However, development of resistance to sorafenib often prevents its long-term efficacy. No validated biomarkers currently exist for appropriately selecting patients with cancer for sorafenib treatment. In the present study, we report that CREB3L2 expression in human breast cancer cell lines is a marker of response to sorafenib. Analysis of human breast cancer cell lines using Oncomine database and Genomics of Drug Sensitivity in Cancer (GDSC) database revealed an association between reduced expression of CREB3L2 and sensitivity to sorafenib. Wet lab experiment in five human breast cancer cell lines confirmed the association between reduced expression of CREB3L2 and sensitivity to sorafenib. Further, reduced expression of CREB3L2 was associated with poor Reccurence Free Survival in Luminal breast cancer. Our results suggest that CREB3L2 expression is a biomarker of response to sorafenib and outcome in breast cancer.

With the support of cellular system a cellular-based mobile ad hoc network (MANET) offers promising communication scenarios while entails secure data exchange as other wireless systems. In this paper, we propose a novel decentralized key generation mechanism using shared symmetric polynomials in which the base stations (BSs) carry out an initial key generation by a symmetric polynomial in a distributed manner and then pass on the key material to mobile stations (MSs). Thereafter, our proposed key generation scheme enables each pair of MSs to establish a pairwise key without any intervention from the BS, thus reducing the management cost for the BS. The shared key between two MSs is computed without any interaction between them. In addition, the trust among MSs is derived from the cellular infrastructure, thus enjoying an equal security level as provided in the underlying cellular network. Simulations are done to observe the system performance and the results are very encouraging.

Currently, inspection of remote environments poses potential hazards to the human operator. Mixed reality presents the unique opportunity for the human operators & the piloted robots to co-exist within a virtual world that is then spatially mapped to the physical world. This can then act as a platform to conduct remote inspection, where the human operators could potentially possess heightened situational awareness and control.