VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging

Visual attribution (VA) in relation to medical images is an essential aspect of modern automation-assisted diagnosis. Since it is generally not straightforward to obtain pixel-level ground-truth labelling of medical images, classification-based interpretation approaches have become the de facto standard for automated diagnosis, in which the ability of classifiers to make categorical predictions based on class-salient regions is harnessed within the learning algorithm. Such regions, however, typically constitute only a small subset of the full range of features of potential medical interest. They may hence not be useful for VA of medical images where capturing all of the disease evidence is a critical requirement. This hence motivates the proposal of a novel strategy for visual attribution that is not reliant on image classification. We instead obtain normal counterparts of abnormal images and find discrepancy maps between the two. To perform the abnormal-to-normal mapping in unsupervised way, we employ a Cycle-Consistency Generative Adversarial Network, thereby formulating visual attribution in terms of a discrepancy map that, when subtracted from the abnormal image, makes it indistinguishable from the counterpart normal image. Experiments are performed on three datasets including a synthetic, Alzheimer’s disease Neuro imaging Initiative and, BraTS dataset. We outperform baseline and related methods in both experiments

A novel hybrid MPPT technique for solar PV applications using perturb & observe and fractional open circuit voltage techniques

Solar photovoltaic (PV) systems have been an area of active research for the last few decades to improve the efficiency of solar PV module. The non-linear nature of IV curve of solar PV module demands some technique to track the maximum voltage and maximum current point on IV curve corresponding to Maximum Power Point(MPP). Thus, Maximum Power Point Tracking (MPPT) techniques are widely deployed for this purpose. Lot of MPPT techniques have been developed in recent past but still most commercial systems utilizes the perturb & observe (P&O) MPPT technique because of its simple algorithm, low cost and ease of implementation. However, this technique is slow in tracking MPP under rapidly changing irradiance conditions and it also oscillates around the MPP. This paper addresses this problematic behavior of P&O technique and hence presents a novel MPPT hybrid technique that is combination of two basic techniques i.e. P&O and Fractional Open Circuit Voltage (FOCV) technique in order to overcome the inherited deficiencies found in P&O technique. The proposed MPPT technique is much more robust in tracking the MPP even under the frequent changing irradiance conditions and is less oscillatory around the MPP as compared to P&O. The technique is verified using MATLAB/SIMULNK and simulation results show a clear improvement in achieving the MPP when subjected to change in irradianc

Image fusion using multivariate and multidimensional EMD.

We present a novel methodology for the fusion of multiple (two or more) images using the multivariate extension of empirical mode decomposition (MEMD). Empirical mode decomposition (EMD) is a data-driven method which decomposes input data into its intrinsic oscillatory modes, known as intrinsic mode functions (IMFs), without making a priori assumptions regarding the data. We show that the multivariate and multidimensional extensions of EMD are suitable for image fusion purposes. We further demonstrate that while multidimensional extensions, by design, may seem more appropriate for tasks related to image processing, the proposed multivariate extension outperforms these in image fusion applications owing to its mode-alignment property for IMFs. Case studies involving multi-focus image fusion and pan-sharpening of multi-spectral images are presented to demonstrate the effectiveness of the proposed method

SARS‐CoV‐2 research using human pluripotent stem cells and organoids

Experimental cell models are indispensable for clarifying the pathophysiology of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and for developing therapeutic agents. To recapitulate the symptoms and drug response of COVID-19 patients in vitro, SARS-CoV-2 studies using physiologically relevant human embryonic stem (ES)/induced pluripotent stem (iPS) cell-derived somatic cells and organoids are ongoing. These cells and organoids have been used to show that SARS-CoV-2 can infect and damage various organs including the lung, heart, brain, intestinal tract, kidney, and pancreas. They are also being used to develop COVID-19 therapeutic agents, including evaluation of their antiviral efficacy and safety. The relationship between COVID-19 aggravation and human genetic backgrounds has been investigated using genetically modified ES/iPS cells and patient-derived iPS cells. This review summarizes the latest results and issues of SARS-CoV-2 research using human ES/iPS cell-derived somatic cells and organoids

Evaluation of pre-analytical factors affecting plasma DNA analysis.

Pre-analytical factors can significantly affect circulating cell-free DNA (cfDNA) analysis. However, there are few robust methods to rapidly assess sample quality and the impact of pre-analytical processing. To address this gap and to evaluate effects of DNA extraction methods and blood collection tubes on cfDNA yield and fragment size, we developed a multiplexed droplet digital PCR (ddPCR) assay with 5 short and 4 long amplicons targeting single copy genomic loci. Using this assay, we compared 7 cfDNA extraction kits and found cfDNA yield and fragment size vary significantly. We also compared 3 blood collection protocols using plasma samples from 23 healthy volunteers (EDTA tubes processed within 1 hour and Cell-free DNA Blood Collection Tubes processed within 24 and 72 hours) and found no significant differences in cfDNA yield, fragment size and background noise between these protocols. In 219 clinical samples, cfDNA fragments were shorter in plasma samples processed immediately after venipuncture compared to archived samples, suggesting contribution of background DNA by lysed peripheral blood cells. In summary, we have described a multiplexed ddPCR assay to assess quality of cfDNA samples prior to downstream molecular analyses and we have evaluated potential sources of pre-analytical variation in cfDNA studies

INDIGO : better geomagnetic observatories where we need them

The INDIGO project aims to improve the global coverage of digital observatories by deploying digital magnetometer systems in: i) Observatories where existing analog recording equipment is in need of upgrading. ii) Newly established digital observatories. iii) Existing digital observatories for the purpose of quality control and redundancy. In implementing the project and selecting suitable sites, special attention is paid to parts of the Earth devoid of magnetic observatories, increasing the reliability and long-term operation of existing observatories and cost-effective use of local resources. The Poster reviews the current status of the project. We examine the different steps and initiatives taken since the initiation of INDIGO in 2004 and assess their effectiveness in achieving progress towards our aims of improving global coverage and enhanced data quality

New Longitudinal Waves in Electron-Positron-Ion Quantum Plasmas

A general quantum dispersion equation for electron-positron(hole)-ion quantum plasmas is derived and studied for some interesting cases. In an electron-positron degenerate Fermi gas, with or without the Madelung term, a new type of zero sound waves are found. Whereas in an electron-hole plasmas a new longitudinal quantum waves are revealed, which have no analogies in quantum electron-ion plasmas. The excitation of these quantum waves by a low-density monoenergetic straight electron beam is examined. Furthermore, the KdV equation for novel quantum waves is derived and the contribution of the Madelung term in the formation of the KdV solitons is discussed

An Appraisal of the Current Scenario in Vaccine Research for COVID-19

The recent coronavirus disease 2019 (COVID-19) outbreak has drawn global attention, affecting millions, disrupting economies and healthcare modalities. With its high infection rate, COVID-19 has caused a colossal health crisis worldwide. While information on the comprehensive nature of this infectious agent, SARS-CoV-2, still remains obscure, ongoing genomic studies have been successful in identifying its genomic sequence and the presenting antigen. These may serve as promising, potential therapeutic targets in the effective management of COVID-19. In an attempt to establish herd immunity, massive efforts have been directed and driven toward developing vaccines against the SARS-CoV-2 pathogen. This review, in this direction, is aimed at providing the current scenario and future perspectives in the development of vaccines against SARS-CoV-2

An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service

Internet of Things (IoT) is expected to play a major role in our lives through pervasive systems of sensor networks encompassing our environment. These systems are designed to monitor vital physical phenomena generating data which can be transmitted and saved at cloud from where this information can be accessed through applications and further actions can be taken. This paper presents the implementation and results of an environmental monitoring system which employs sensors for temperature and humidity of the surrounding area. This data can be used to trigger short term actions such as remotely controlling heating or cooling devices or long term statistics. The sensed data is uploaded to cloud storage and an Android application accesses the cloud and presents the results to the end users. The system employs Arduino UNO board, DHT11 sensor, ESP8266 Wi-Fi module, which transmits data to open IoT API service ThingSpeak where it is analyzed and stored. An Android application is developed which accesses the cloud and displays results for end users via REST API Web service. The experimental results show the usefulness of the system

Enhanced oral bioavailability and hepatoprotective activity of thymoquinone in the form of phospholipidic nano-constructs

Background: The poor biopharmaceutical properties of thymoquinone (TQ) obstruct its development as a hepatoprotective agent. To surmount the delivery challenges of TQ, phospholipid nanoconstructs (PNCs) were constructed. Method: PNCs were constructed employing microemulsification technique and systematic optimization by three-factor three level Box-Behnken design. Result: Optimized PNC composition exhibited nano size (90%), controlled drug release pattern, and neutral surface charge (zeta potential of −0.65 mV). After oral administration of a single dose of PNC, it showed a relative bioavailability of 386.03% vis-à-vis plain TQ suspension. Further, TQ-loaded PNC demonstrated significant enhanced hepato-protective effect vis-à-vis pure TQ suspension and silymarin, as evidenced by reduction in the ALP, ALT, AST, bilirubin, and albumin level and ratified by histopathological analysis. Conclusion: TQ-loaded PNCs can be efficient nano-platforms for the management of hepatic disorders and promising drug delivery systems to enhance oral bioavailability of this hydrophobic molecule