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Recurrent tracheoesophageal fistula is a rare complication of esophageal atresia surgery with an incidence of 3-15%. The presentation is subtle and is often missed, presenting as choking episodes during feed and recurrent chest infections. It is both a diagnostic and management challenge and requires a dedicated multidisciplinary pediatric surgical setup with adequate infrastructure for optimal management. We present a case of recurrent tracheoesophageal fistula which was diagnosed at our center. The patient underwent successful surgical management and is thriving well at six months follow-up period. [ABSTRACT FROM AUTHOR]

Evolving insect resistance to delta-endotoxins can be delayed by using a few strategies like high dosage, refugia, and gene stacking which require the expression of delta-endotoxins at sufficiently high levels to kill the resistant insects. In this study, we comparatively analyzed the efficacy of targeting truncated cry1Ac protein to the cytoplasm, endoplasmic reticulum (ER), and chloroplast to obtain high protein expression. mRNA and protein profiling of cry1Ac showed that both ER and chloroplast are efficient targets for expressing high levels of truncated cry1Ac. A maximum of 0.8, 1.6, and 2.0% cry1Ac of total soluble protein were obtained when the truncated cry1Ac was expressed in the cytoplasm, routed through ER, and targeted to the chloroplast. We further showed that not only the protein content but also the biological activity of truncated cry1Ac increases by sub-cellular targeting and the biological activity is slightly greater in the ER routed transgenic lines by conducting different bioassays on Helicoverpa armigera. Using native Western analysis, we demonstrated that the truncated cry1Ac protein could exist as oligomers in plant cells and this oligomerization capability is low in the cytoplasm. In conclusion, routing of delta endotoxins through ER is the first choice to obtain high protein expression and bioactivity. [ABSTRACT FROM AUTHOR]

Low-cost potable water is a challenge nowadays and research are going on based on the solar thermal technique in order to bring down the cost of yield worldwide. The evacuated tubular collector is generally used to achieve higher collector efficiency. A higher rate of vaporization is achieved by integrating a number of series-connected evacuated tubes with solar still. In this work, the effect of heat transfer rate on the performance of evacuated tubular collector systems and natural evaporation and condensation-based freshwater yield in free and forced modes is reviewed. The performance of the evacuated tube is affected by varying tilt angles and an increase in the intensity of solar radiation. The overall performance of the system depends on the heat transfer rate from the absorber tube to the working fluid. The heat transfer rate can further be improved by adding various sizes of nanoparticles to working fluid and act as a heat-absorbing device during less sunshine or cloudy days. Previous finding such as operating temperature and flow behavior inside evacuated tube collector, daily freshwater yield variation with respect to depth of water inside solar still, system efficiency of the integrated system and variation in energy and exergy efficiency are presented step by step throughout the paper. [ABSTRACT FROM AUTHOR]

Unplanned urbanization in developing cities has led to haphazard development, increasing disaster risks in densely populated areas. Despite being more disaster-prone, these dense settlements often lack essential emergency services. Delivering emergency services requires a high-resolution geodatabase of minor roads and their attributes, such as road width. Such information can be a crucial input for quantifying emergency vehicles (EVs) access in an area. To the best of our knowledge, no research has addressed this problem statement, making this paper the first to propose quantifying road width and using it to develop EVs accessibility maps. We propose a two-stage process to achieve these goals. In the first stage, we designed a deep neural network (DNN) to accurately organize built-up features, including minor roads using true-colour high-resolution satellite imagery of Pleiades-1A for Mumbai. In the second stage, we propose an algorithm to calculate road width and then compare it with EVs dimension (width) details to develop high-resolution accessibility maps. The model classified features with around 91.88% accuracy along with 90% Kappa coefficient, while the classification accuracy of minor roads in informal settlements was about 85.80%. We found that informal settlements have much less access to various EVs compared to formal settlements due to mixed signatures. The outcomes presented in this paper can be used as a decision-making tool to develop a geodatabase of road widths and EVs accessibility maps for efficient resource planning, which can ultimately lead to the development of disaster-resilient cities. • Extraction of minor-roads in dense informal settlements using Deep Learning (DL). • Algorithm to estimate road width from classified imagery. • Generation of high-resolution Emergency Vehicle (EV) accessibility maps for Mumbai, India. [ABSTRACT FROM AUTHOR]

Energy planning in many developing Asian countries has neglected the structure of energy demand and how it is likely to evolve as development takes its path. The limited availability of energy consumption data makes it very difficult to assess the energy savings potential at end-use level. Energy planning requires the formulation of a proper modelling framework that supports the definition of energy policies. From the different approaches available, Input - Output (I-O) models are particularly useful, since they allow considering different impacts that can be consistent with different energy policy options. This paper proposes a novel I-O modelling framework by introducing a bottom-up approach into an I-O model which is combined with technical data for the holistic assessment of energy efficient technologies in the residential sector, which can assist energy decision-makers of India on the appraisal of the future impacts of the current national energy saving targets. A large size platform of real data has also been gathered considering different data sources, namely the household building stock characterization, the number of operating days according to the climatic regions of India, the lifetime and the investment cost of equipment. Finally, the main results are discussed and future research opportunities are identified. [ABSTRACT FROM AUTHOR]

Fusarium wilt is one of the most prevalent and damaging diseases of tomato. Among various toxins secreted by the Fusarium oxysporum f. sp. lycopersici (causal agent of Fusarium wilt of tomato), fusaric acid (FA) is suspected to be a potent pathogenicity factor in tomato wilt disease development. With this rationale the present study was carried out with physiological, biochemical and proteomic perspectives. Treatment of FA was given to the leaves of tomato directly through infiltration to show the characteristic features of Fusarium wilt of tomato. The phytotoxic effect of FA was assessed in the form of cell death in tomato leaves which was observed by increased uptake of Evans blue stain. The measurement of electrolyte leakage was used as an indicator of the extent of cell death. The influence of FA on the leaf photosynthesis of tomato plant was investigated and it was found that FA strongly reduced the photosynthetic pigment contents of tomato leaves resulting to heavy suppression of leaf photosynthesis processes, which therefore affected leaf physiology finally leading to leaf wilting and necrosis. This cell death inducer (FA) produced an enormous oxidative burst during which large quantities of reactive oxygen species (ROS) like H 2 O 2 was generated in the treated leaf tissues of tomato plants which was evident from enhancement in lipid peroxidation. To assess the involvement of proteolysis in the cell death cascade induced by FA treatment, total protease activity was measured in the leaf tissues and it was found that the total protease activity increased with the treatment and leading to cell death. Furthermore, proteomic study was used as a powerful tool to understand the alterations in cellular protein expression in response to FA exposure. Differential expression in several proteins was observed in the present study. Proteomic analyses, thus, clearly indicate that proteins belonging to different functional classes are significantly affected in the plant leaf tissues after FA exposure leading to deterioration of structure and metabolism of cells. Thus, it is concluded that FA plays an important role in fungal pathogenicity by decreasing cell viability. [ABSTRACT FROM AUTHOR]

The present study was aimed at analyzing uninfected wheat grain ( Triticum aestivum ) and wheat seed gall nematodes ( Anguina titici ) by means of wave-dispersive X-ray fluorescence (WD-XRF), Fourier transform infrared (FTIR) spectroscopy and Diffuse reflectance spectroscopy to assess their elemental and molecular compositions. WD-XRF was used to detect and quantify the major and trace elements in the tested samples. The minerals detected and quantified in no-infection, low infection and high infection wheat samples were potassium (K), sulphur (S), phosphorus (P), chlorine (Cl), calcium (Ca), magnesium (Mg), iron (Fe), silicon (Si), and zinc (Zn). Copper (Cu) was only observed in wheat samples showing no-infection and high infection. Chromium (Cr), manganese (Mn), and nickel (Ni) were detected only in wheat samples of low infection and high infection, and it was completely absent in uninfected wheat samples. Sodium (Na) was only detected in low infection samples whereas aluminium (Al) was only found in high infection wheat samples. The concentrations of these elements in different wheat samples varied. FTIR spectroscopy was used to study the molecular compositions of the infected and uninfected wheat grain samples. Diffuse reflectance measurements of uninfected and infected wheat samples were used to identify spectral differences among wheat samples. In the present investigation a detailed comparison of these samples has been presented at the elemental and molecular levels. [ABSTRACT FROM AUTHOR]

Today, the influence of information technology has been spreading exponentially, from high level research going on in top labs of the world to the home appliances. Such a huge demand is compelling developers to develop more software to meet the user expectations. As a result reliability has come up as a critical quality factor that cannot be compromised. Therefore, researchers are continuously making efforts to meet this challenge. With this spirit, authors of the paper have proposed a highly structured framework that guides the process of quantifying software reliability, before the coding of the software start. Before presenting the framework, to realize its need and significance, the paper has presented the state-of-the-art on software reliability quantification. The strength of fuzzy set theory has been utilized to prevail over the limitation of subjectivity of requirements stage measures. Salient features of the framework are also highlighted at the end of the paper. [ABSTRACT FROM AUTHOR]

Poor solidification or energy release rate from PCM has been a long-withstanding problem of PCM heat sinks which prevents their application to wide range of problems. The present study aims to simultaneously improve the energy release and storage rate of PCM by employing a novel PCM heat sink with double-sided spiral fins. The novel configuration optimizes the energy stored by the module in latent and sensible form thus improving both energy storage and release rate in successive operating cycles, unlike previous studies which solely focuses on either of these rates. Such a configuration will ensure that the system reaches the initial conditions before the start of successive cycle. The effect of various parameters of spiral fins on the heat sink's performance is numerically investigated and it is observed that increasing fin diameter (2 mm ≤ d fin ≤ 4 mm) and fin length (1 mm ≤ L fin ≤ 3 mm) reduces the critical temperature of source and the time taken by the system to reach initial conditions. Best configuration of spiral fins is then compared with traditional pin fins to highlight that the critical temperature and time taken to reach initial conditions reduces by 7 K (2%) and 19 mins (38%) respectively. • Performance of PCM heat sink with double sided spiral fin are compared to pin fin. • Heat sink with spiral fins stores energy in latent form with better efficiency. • Spiral fins further reduces the source temperature by 8 K (2.4%) over pin fins. • Time taken by heat sink with spiral fin to reach initial conditions reduces by 33%. • Heat sink with spiral fin performs better across different operating conditions. [ABSTRACT FROM AUTHOR]

In copy move forgery, some area of image is copied and pasted elsewhere in the same image. This may contain two motives, either to increase number of objects or to hide objects. In this paper, we propose a fast copy—rotate-paste image forgery detection method using rotation invariant property of Zernike moments (ZM). We have focused on those images where number of objects as a result of forgery, gets increased. We performed connected component analysis before block matching to reduce time complexity and for more reliable results. The fraudster attempts to make its appearance more like original. Copied portion can be scaled, rotated or colored before pasting it. To overcome these problems ZM is used. Experimental results show that the proposed image forgery detection method performs better for rotated and scaled blocks. [ABSTRACT FROM AUTHOR]

• Power law distribution in social media mentions (altmetrics) to scientific articles is explored. • Empirical analysis confirms that altmetrics exhibit power law. • Some other fits like log-normal are found to be plausible, whereas exponential distribution does not show a good fit. • Implications of existence of Power law in altmetrics are discussed. Power Laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit are determined, both using least squares regression as well as the Maximum Likelihood Estimate (MLE) approach. We also explore the fit of the mention data to other distribution families like the Log-normal and exponential distributions. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles. The Log-normal distribution also looks plausible but is not found to be statistically significant, and the exponential distribution does not show a good fit. Major implications of power law in altmetrics are given and interesting research questions are posed in pursuit of enhancing the reliability of altmetrics for research evaluation purposes. [ABSTRACT FROM AUTHOR]

Magnesium borates (MB) nanorods are widely used as reinforcing materials due to high mechanical strength and resilience to heat and corrosion. However, difficulties in synthesis of single-phase MB continue to reduce the mechanical properties of composites. Herein, a single-phase Magnesium borate (Mg 2 B 2 O 5) in the form of nanorod has been synthesized using an optimized molar ratio of magnesia (MgO) and boric acid (H 3 BO 3) by combining solution reaction and sintering process. Phase and microstructural changes of as synthesized magnesium borate (MB) crystals during the reaction sintering process in between 700 °C and 1200 °C has been closely examined with the help of various characterization techniques. Phases of magnesium borate starts appearing on thermal treatment above 700 °C. Phase transformation of magnesium borate from monoclinic to orthorhombic followed by triclinic was also observed during the thermal treatment. The optimum temperature to obtain pure triclinic Mg 2 B 2 O 5 phase was found to be in between 1100°C and 1200 °C. [ABSTRACT FROM AUTHOR]

Mushy zone constant is an important parameter of the enthalpy-porosity method which defines how the flow evolves within the cavity. The present numerical study investigates the effect of mushy zone constant (C = 1 × 104, 1 × 106 & 1 × 108 kg m−3 s−1) on the melting phenomena of a phase change material (PCM) under hyper-gravity conditions (2 g , 5 g , 10 g and 20 g). Value of 'C' influences the fluid velocity within the liquid region of PCM and its mobility during phase change process. The melting rate is enhanced by 32% as value of 'C' is reduced from 1 × 108 to 1 × 104 at normal gravitational conditions while heat transfer rate at hot wall improves as value of 'C' is decreased from 1 × 108 to 1 × 104 with maximum discrepancy being 83%. Under the combined effect of value of 'C' and hyper-gravity, the movement of liquid region within the cavity becomes distinct in comparison to the normal gravity condition. The melting rate is enhanced by 74% at 'C' = 1 × 104 & by 54% at 'C' = 1 × 104 as the convection within the liquid region also enhances. • Effect of mushy zone constant (C) & hyper-gravity on PCM's melting is investigated. • Increase in value of C reduces the mobility and velocity of fluid region. • Melting rate (MR) increases by 32% as value of C is reduced from 108 to 104. • MR increases by a max. of 74% as gravitational acceleration is increased to 20 g. • Heat transfer improves as value of C is reduced under hyper-gravity. [ABSTRACT FROM AUTHOR]

Abstract: Cryopreserved semen is commonly used for assisted reproduction in livestock including cattle. However, spermatozoa undergo numerous physiological and biochemical changes during freezing and thawing process that affects their fertilizing ability. The aim of present study was to improve the post thaw quality of crossbreed cattle “Karan Fries” (Holstein-Friesian×Tharparkar) spermatozoa. A total of nine ejaculates from three randomly chosen Karan Fries bulls were extended and cryopreserved in Tris–egg yolk citrate (EYTC) extender supplemented with 50mM Taurine or 100mM Trehalose. Semen samples cryopreserved without these additives in EYTC extender were taken as a control. Cryopreserved semen were thawed and assessed for semen quality parameters like sperm motility, viability and plasma membrane integrity. Extent of capacitation was measured by estimating the number of sperm that underwent an acrosome reaction with Lysophosphatidyl choline (LPC) addition by dual staining with giemsa and trypan blue stains. Oxidative stress in terms of rate of H2O2 production and membrane lipid peroxidation were assessed in spermatozoa. Intracellular calcium concentration was also measured using fluorescent dye Fura-2AM. Post-thaw semen evaluation showed that supplementation of Taurine or Trehalose to EYTC extender significantly (P <0.05) increased motility, viability and membrane integrity of spermatozoa. Percentage of cryocapacitated spermatozoa was also significantly (P <0.05) decreased in presence of these additives. Similarly, rate of H2O2 production, lipid peroxidation and intracellular calcium were found to be significantly (P <0.05) higher in spermatozoa cryopreserved in absence of these additives. The results obtained clearly indicated that supplementation of Taurine or Trehalose to EYTC extender prior to cryopreservation improves Karan Fries sperm quality. [Copyright &y& Elsevier]

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous, as well as the highly variable tumor sizes and shapes. This article proposes an efficient automated method for tumor segmentation in BUS images based on a contextual information-aware conditional generative adversarial learning framework. Specifically, we exploit several enhancements on a deep adversarial learning framework to capture both texture features and contextual dependencies in the BUS images that facilitate beating the challenges mentioned above. First, we adopt atrous convolution (AC) to capture spatial and scale context (i.e., position and size of tumors) to handle very different tumor sizes and shapes. Second, we propose the use of channel attention along with channel weighting (CAW) mechanisms to promote the tumor-relevant features (without extra supervision) and mitigate the effects of artifacts. Third, we propose to integrate the structural similarity index metric (SSIM) and L1-norm in the loss function of the adversarial learning framework to capture the local context information derived from the area surrounding the tumors. We used two BUS image datasets to assess the efficiency of the proposed model. The experimental results show that the proposed model achieves competitive results compared with state-of-the-art segmentation models in terms of Dice and IoU metrics. The source code of the proposed model is publicly available at https://github.com/vivek231/Breast-US-project. • Efficient automated method for tumor segmentation in breast ultrasound (BUS) images. • Contextual information-aware conditional generative adversarial learning framework. • Capture spatial and scale context to handle very different tumor sizes and shapes. • Two BUS image datasets are used to assess the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

• A conditional generative adversarial network (cGAN) is proposed to segment the breast tumor. • A convolutional neural network (CNN) based shape classification descriptor is proposed. • The segmented regions are classified into four different shapes and correlated with four molecular subtypes. Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns to recognize the tumor area and to create the binary mask that outlines it. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. As a consequence, the proposed method outperforms several state-of-the-art approaches. Our working hypothesis is corroborated by diverse segmentation experiments performed on INbreast and a private in-house dataset. The proposed segmentation model, working on an image crop containing the tumor as well as a significant surrounding area of healthy tissue (loose frame ROI), provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four tumor shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on DDSM, since it provides shape ground truth (while the other two datasets does not), yielding an overall accuracy of 80%, which outperforms the current state-of-the-art. [ABSTRACT FROM AUTHOR]

Hybrid generation and energy storage systems can enhance asset flexibility, enabling various services and optimizing financial performance. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary or too complex for most asset owners to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a deep learning-based battery sizing optimization tool for hybridizing generation facilities. The tool uses deep learning technique to predict revenue over a broad search space of potential battery sizes, estimate capital and operations costs (including accounting for battery degradation), and calculate financial performance of each potential battery system investment; an output is a recommendation of battery that maximizes financial performance. The tool is tested and validated for hydropower generation and is publicly available on Idaho National Laboratory's GitHub page (https://github.com/idaholab/Hydro%5fHybrids), documented in Zenodo (https://zenodo.org/record/7562692#.Y9Q7anbMKUm), and accessible through an intuitive web app (hydrohybrids.inl.gov). This tool will help a greater cross-section of generation owners consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets. [ABSTRACT FROM AUTHOR]

We report the synthesis of nanocrystalline calcium carbonate with varying particle sizes by precipitation techniques from an aqueous solution of calcium nitrate and sodium carbonate at controlled pH. The particle size of the carbonate powder was precisely controlled by changing the precursor concentration. The synthesized carbonate powders were characterized by using scanning electron microscopy, X-ray diffraction technique, and transmission electron microscopy. The particle size, along with the crystallite size of as-synthesized carbonate powder, decreases with increasing precursor concentration. The non-isothermal decomposition kinetics of the carbonate powder was also evaluated by using near to the modified Arrhenius equation's exact solution. The experimental results were best fitted at n = 0.5, and the one-dimensional diffusion-controlled transport process mechanism (D 1) and one-dimensional phase boundary movement mechanism (R 1) was found to be very close fit of the corresponding evaluated g(α) value. The apparent activation energy of the nano calcium carbonate decomposition was found in the range of 120–175 kJ/mol, which is also inherently functioning with the average particle size. The apparent activation energy of decomposition of CaCO 3 found to be decreased with decreasing average particle size of nanocrystalline calcium carbonate. [ABSTRACT FROM AUTHOR]

• An institutional collaboration recommendation system is designed and demonstrated. • The proposed system uses Network Analysis and NLP based framework. • The proposed system determines the research strengths of institutions. The shift from 'trust-based funding' to 'performance-based funding' is one of the factors that has forced institutions to strive for continuous improvement of performance. Several studies have established the importance of collaboration in enhancing the performance of paired institutions. However, identification of suitable institutions for collaboration is sometimes difficult and therefore institutional collaboration recommendation systems can be vital. Currently, there are no well-developed institutional collaboration recommendation systems. In order to bridge this gap, we design a framework that recognizes the thematic strengths and core competencies of institutions, which can in turn be used for collaboration recommendations. The framework, based on NLP and network analysis techniques, is capable of determining the strengths of an institution in different thematic areas within a field and thereby determining the core competency and potential core competency areas of that institution. It makes use of recently proposed expertise indices such as x and x(g) indices for determination of core and potential core competency areas and can toss two kinds of recommendations: (i) for enhancement of strength of strong areas or core competency areas of an institution and (ii) for complementing the potentially strong areas or potential core competency areas of an institution. A major advantage of the system is that it can help to determine and improve the research portfolio of an institution within a field through suitable collaboration, which may lead to the overall improvement of the performance of the institution in that field. The framework is demonstrated by analyzing the performance of 195 Indian institutions in the field of 'Computer Science'. Upon validation using standard metrics for novelty, coverage and diversity of recommendation systems, the framework is found to be of sufficient coverage and capable of tossing novel and diverse recommendations. The article thus presents an institutional collaboration recommendation system which can be used by institutions to identify potential collaborators. [ABSTRACT FROM AUTHOR]

Ecotropic viral integration site-1 (EVI1) is an oncogenic zinc finger transcription factor whose expression is frequently upregulated in a variety of cancers, including both myeloid malignancies and solid tumors. Previously, our group has shown that EVI1 knockdown minimizes the metastatic potential of colon cancer cells compared to that of control cells. In this study, to identify the potential targets that regulate cancer metastasis, control and EVI1 knockdown colon cancer cells were subjected to microarray. Differential gene expression analysis revealed significant downregulation of tissue inhibitor of matrix metalloproteinase-2 (TIMP2) in EVI1 expressing cells. EVI1 knockdown increased TIMP2 protein expression levels and reduced wound healing and migration capacity in metastatic cells. Mechanistically, the TIMP2 promoter harbors potential binding sites for EVI1; EVI1 binds to TIMP2 promoter and represses its expression, as observed using ChIP and luciferase assay, respectively. TIMP2 is an important metastasis suppressor gene; however, its function is suppressed in many cancers through hypermethylation. Thus, demethylation could prove to be a potential alternative to reactivate TIMP2 functional activity. Immunoprecipitation analysis showed that DNA-methyltransferase 1 (DNMT1), which plays a vital role in maintaining the genome methylation pattern during DNA replication and repair, interacts with EVI1 to promote TIMP2 silencing. Treating cancer cells in vitro with a known demethylation agent, 5-aza-2′-deoxycytidine (Aza-D), restored the optimal TIMP2 expression without altering EVI1 binding efficiency and reduced relative wound healing potential of cancer cells. Animal studies showed that Aza-D treated cells injected through the intravenous route exhibited reduced liver and skin metastasis when compared to non-treated cells. Furthermore, Aza-D treatment in mice delayed the metastasis progression compared to the vehicle treated group. Thus, the present study provides an insight into the therapeutic applications of demethylating agents to reduce cancer metastasis in models with EVI1 overexpressing tumors. [Display omitted] [ABSTRACT FROM AUTHOR]

Miniaturization of circuitry has inherently led to higher heat dissipation which has to be controlled to avoid temperature fluctuation ensuring smooth functioning of the system where developments in the field of PCM provides flexible solutions for avionic Thermal Management (TM). This article focuses to analyse thermal performance of novel tapered triangular finned heat sink along with Vacuum Insulation Panels (VIPs) tamped with nanoalloy (n-LMA) enhanced organo-metallic Mn based layered perovskite Solid-Solid PCM (SS-PCM) to its compatibility for passive TM satellite auxiliary system. An inclusion of 5 wt% nanoalloy (Nano-encapsulated liquid Ga:In eutectic alloy) in SS-PCM increased the thermal conductivity by 21.05% (0.374 W/m-K) and charging and discharging enthalpy by 16.84% (74.56 J/g) and 17.61% (76.32 J/g) respectively. Thermo-physical property values were obtained through Field Emission Scanning Electron Microscope, Scanning Electron Microscope, Differential Scanning Calorimetry and Laser Flash Apparatus. Transient thermal performance analysis was carried out in tapered triangular finned heat sink with specific duty cycles (600s/5400s, 1200s/4800s and 1800s/4200s) for heat fluxes (2 W, 4 W, 6 W, 8 W and 10 W) and with/without VIP for different aspects of SS-PCM and SS-PCM/n-LMA. The effect of n-LMA influences the enhancement in thermal storage and heat transfer performance attributes with a maximum temperature drop of 6.98% over pure SS-PCM. Experimental study reveals better temperature reduction with a maximum of 53.81% decrement in overall heat sink temperature and VIP manifests unidirectional heat transfer through the heat sink reducing overall spatial temperature variation. Further, numerical validation was performed for obtained results with COMSOL application, demonstrating the suitability for real-time passive TM in satellite avionics space applications. • Nanoalloy enhanced Solid-Solid PCM for satellite avionics thermal management. • Encapsulation of 5 wt% liquid metal alloy in Mn layered perovskite as SS-PCM/n-LMA. • Nanoalloy enhanced heat storage and conduction in SS-PCM by 17.61% and 21.05%. • Vacuum insulation panel covered tapered triangular finned heat sink as passive TM. • Study reveals better energy storage characteristics by 53.8% decrement in T max. [ABSTRACT FROM AUTHOR]

The performance comparison of various flow fields for practical application can be better understood when loading the cell at a lower voltage region for an operational duration of more than an hour. In this paper, the performance of serpentine and serpentine with tapered channels are compared by loading the Polymer Electrolyte Membrane (PEM) fuel cell at a constant voltage of 0.5 V for an operational duration of 5 h. Purging with nitrogen at the inlets is done to recover the drop in current over the period of operation and flush out water. The presence of tapered flow fields in a serpentine flow channel shows an improvement of 15% in the current obtained due to better reactant gas transport in the gas diffusion layer. The performance of both flow fields were studied with polarization and power density curve obtained after 5 h of testing. Further, to confirm the performance improvement at lower voltage region, impedance study is done and obtained Nyquist plot confirms the better transport phenomenon of reactant gases to catalyst site and better removal of water in Gas Diffusion Layer (GDL). Image 1 • PEMFC with serpentine tapered flow channel has higher performance. • Performance comparison for 5 h at a constant loading of 0.5 V has been studied. • Purging at inlets helps to remove water from flow channel and GDL. • The improvement in performance has been further validated with Nyquist plot. [ABSTRACT FROM AUTHOR]

Increasing land degradation by high level of metal wastes is of prime concern for the global research communities. In this respect, halophytes having specific features like salt glands, exclusion of excess ions, heavy metals (HMs) compartmentalization, large pool of antioxidants, and associations with metal-tolerant microbes are of great promise in the sustainable clean-up of contaminated sites. However, sustainable clean-up of HMs by a particular halophyte plant species is governed considerably by physico-chemical characteristics of soil and associated microbial communities. The present review has shed light on the superiority of halophytes over non-halophytes, mechanisms of metal-remediation, recent developments and future perspectives pertaining to the utilization of halophytes in management of HM-contaminated sites with the aid of bibliometric analysis. The results revealed that the research field is receiving considerable attention in the last 5–10 years by publishing ∼50–90% documents with an annual growth rate of 15.41% and citations per document of 29.72. Asian (viz., China, India, and Pakistan) and European (viz., Spain, Portugal, Belgium, Argentina) countries have been emerged as the major regions conducting and publishing extensive research on this topic. The investigations conducted both under in vitro and field conditions have reflected the inherent potential of halophyte as sustainable research tool for successfully restoring the HM-contaminated sites. The findings revealed that the microbial association with halophytes under different challenging conditions is a win-win approach for metal remediation. Therefore, exploration of new halophyte species and associated microorganisms (endophytic and rhizospheric) from different geographical locations, and identification of genes conferring tolerance and phytoremediation of metal contaminants would further advance the intervention of halophytes for sustainable ecological restoration. [Display omitted] • Bibliometric analysis on halophyte assisted HMs remediation was carried out. • Potential implications of halophyte in restoration of HMs contaminated sites is explored. • Contribution of halophyte-assisted microbes in improved remediation is highlighted. • Influence of environmental factors on halophyte aided remediation has been identified. • Future research perspectives on halophyte-mediated phytoremediation is highlighted. [ABSTRACT FROM AUTHOR]

Breast cancer chemoprevention has become increasingly important in India as it faces a potential breast cancer epidemic over the next decade. Curcumin, the active ingredient in turmeric is a well known chemopreventive agent that possesses various therapeutic properties including antioxidants and anti-inflammatory effects. In the present study, we have investigated the inhibitory effects of BDMC-A, an analog of curcumin, on invasion, angiogenesis and metastasis markers using in vitro with MCF-7 cells and in silico studies, hence proved that BDMC-A has more potential than curcumin. Mechanistic studies revealed that BDMC-A might have exerted its activity by inhibiting metastatic and angiogenic pathways by modulating the expression of proteins upstream to NF-κB (TGF-β, TNF-α, IL-1β and c-Src), and NF-κB signaling cascade (c-Rel, COX-2, MMP-9, VEGF, IL-8) and by upregulating TIMP-2 levels. An in silico molecular docking study with NF-κB revealed that the docking score and interaction of BDMC-A with NF-κB–DNA binding was more efficient when compared to curcumin. Our overall results showed that BDMC-A more effectively inhibited invasion, angiogenesis and metastasis markers compared to curcumin. The activity can be attributed to the presence of hydroxyl group in the ortho position in its structure. Further research are going on to prove its potential as a therapeutic agent for breast cancer. [ABSTRACT FROM AUTHOR]

Highlights: [•] Anticancer effect of BDMC-A was more potent than curcumin in breast cancer cells. [•] BDMC-A induced apoptosis through both intrinsic and extrinsic pathways. [•] We demonstrated the interactions of BDMC-A and curcumin with PI3K in silico. [ABSTRACT FROM AUTHOR]

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications. • A lightweight and fully automatic skin lesion segmentation model is proposed. • A multiscale mechanism is introduced to extract features at different scales. • The position attention module controls the spatial inter-dependencies. • The channel attention module controls the channel inter-dependencies. • The combination of binary cross-entropy, Jaccard index, and L1 loss is used. [ABSTRACT FROM AUTHOR]