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Accelerated ageing of cross-linked polyethylene (XLPE) insulated aerial bundled cables (ABC) installed at coastal regions for electric power distribution is a topic of major concern. Sudden failures caused by rapid insulation deterioration initiate unexpected power shutdowns. Accurate prognosis of incipient failures in cables will enable maintenance agencies to plan repair or replacement activities well on time thus improving the reliability and availability of the transmission grid. In this research work, historical non-destructive evaluation (NDE) data was generated using infrared thermography of ABCs to ascertain cable degradation parameter. Historical NDE data embodies the progressive degradation experienced by the ABCs installed at harsh marine environment. The degradation growth in cable insulation, when subjected to rough environmental conditions, is non-linear coupled with non-Gaussian / multimodal noise distributions. Therefore, a renowned nonlinear Bayesian estimator namely Particle Filter (PF) is applied on the historical database to determine degradation growth evolution over time. The proposed framework is further complimented with f-step prediction scheme in future time to quantify the prediction accuracy of cable degradation growth in future where measurement data is not available. The so predicted results are then compared to the actual degradation in future. The prediction accuracy demonstrates the efficacy of proposed technique for prognosis of ABCs installed at different locations. Accurate state prediction in different life phases of ABCs further displays robustness of proposed technique in estimating actual degradation growth during the different life stages. [ABSTRACT FROM AUTHOR]

Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications. [ABSTRACT FROM AUTHOR]

In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challenging due to inherent variations in appearance, contrast, texture, and blurry lesion boundaries. This research presents a robust approach utilizing a dilated convolutional residual network, which incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. In each dilated convolutional residual block, dilated convolution is employed to broaden the receptive field with varying dilation rates. To improve the spatial feature information of the encoder, we employed an attention-based spatial feature enhancement block in the skip connections. The ASFEB in our proposed method combines feature maps obtained from average and maximum-pooling operations. These combined features are then weighted using the active outcome of global average pooling and convolution operations. Additionally, we have incorporated a guided decoder strategy, where each decoder block is optimized using an individual loss function to enhance the feature learning process in the proposed AD-Net. The proposed AD-Net presents a significant benefit by necessitating fewer model parameters compared to its peer methods. This reduction in parameters directly impacts the number of labeled data required for training, facilitating faster convergence during the training process. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets. We conducted a Wilcoxon signed-rank test to verify the efficiency of the AD-Net. The outcomes suggest that our method surpasses other cutting-edge methods in performance, even without the implementation of data augmentation strategies. [ABSTRACT FROM AUTHOR]

The precise segmentation of skin lesion in dermoscopic images is essential for the early detection of skin cancer. However, the irregular shapes of the lesions, the absence of sharp edges, the existence of artifacts like hair follicles, and marker color make this task difficult. Currently, fully connected networks (FCNs) and U-Nets are the most commonly used techniques for melanoma segmentation. However, as the depth of these neural network models increases, they become prone to various challenges. The most pertinent of these challenges are the vanishing gradient problem and the parameter redundancy problem. These can result in a decline in Jaccard index of the segmentation model. This study introduces a novel end-to-end trainable network designed for skin lesion segmentation. The proposed methodology consists of an encoder-decoder, a region-aware attention approach, and guided loss function. The trainable parameters are reduced using depth-wise separable convolution, and the attention features are refined using a guided loss, resulting in a high Jaccard index. We assessed the effectiveness of our proposed RA-Net on four frequently utilized benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The empirical results validate that our method achieves state-of-the-art performance, as indicated by a notably high Jaccard index. [ABSTRACT FROM AUTHOR]

Land use land cover plays an essential role in altering regional micro and macro climate, diminishing biodiversity, and significantly influences Land Surface Temperature (LST). However, no research has been conducted in the Shigar district to investigate LULC changes, particularly the correlation between LST and vegetation/snow cover at higher elevated regions. Therefore, this study was carried out in the Shigar district of Gilgit Baltistan Region, Pakistan, due to its extreme climatic variability. In this study, we investigated the spatiotemporal variability of LULC and its impacts on LST in the Shigar district, utilizing Landsat data from 1993 to 2020. To study the variable LST, thermal bands of Landsat imagery were used. LULC was determined by classifying the images using the supervised maximum likelihood classification method. The vegetation and snow cover were also extracted by using the indices of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI), respectively. The results showed that the Shigar district had experienced severe land cover variations in the years of 1993, 2003, 2010, and 2020. Specifically, the agricultural area gradually increased from 1993 to 2020. The forest/vegetation cover also increased in the study area. In contrast, snow cover and water bodies were drastically reduced during this period. The overall vegetation increased within the region and the LST values were found to be decreased in 2003 and 2010, whereas the LST rose in 2020. In addition, the correlation between NDVI and LST was found to be significant negative in areas with high vegetation cover. Conversely, it was weakly positive in other areas with minor vegetations. These findings suggest that green cover plays a crucial role in regulating temperature. Additionally, a negative correlation between LST and NDSI was observed with rising elevation. This study will be helpful in landscape and urban planning, watershed management, and policymaking for climate change variability. [ABSTRACT FROM AUTHOR]

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications. [ABSTRACT FROM AUTHOR]

Kohat Plateau has huge hydrocarbon potential and most of the reservoirs are structurally controlled. Panoba Anticline is among the largest anticlines in Kohat Plateau consists of Paleocene to Eocene sequence. In this research petrophysical properties (density, porosity and permeability of the fractures) of reservoir formations esposed in the Panoba Anticline are analyzed. The main and regional compressional stresses on Panoba Anticline are north-south oriented and the other stresses are might be its derivatives. Based on petrophysical properties the formations of Panoba Anticline are analyzed for reservoir potentiality. Petrophysical properties varies from formation to formation as well as within a same formation because of the fractures behaviors. The reservoir types were assigned to reservoir formation by comparing the results on qualitative basis with Nelson (2001) naturally fractured reservoir (NFR) system. The reservoir character of Kohat Formation is Type 4 to Type 2; Shekhan Formation Type 3 to Type 1; Patala formation Type 4 to Type 3, while the Lockhart Formation has Type 3 to Type 1 reservoir character. In conclusion the petrophysical analysis of Panoba anticline offers a valuable insight into the formations reservoir potentiality which contributes a better understanding of the hydrocarbon prospects in the Kohat Plateau. [ABSTRACT FROM AUTHOR]

This study aimed to examine the potential antioxidant properties of pea peel powder (PPP) in minimizing lipid and protein oxidation, microbial count, and changes in overall quality of buffalo meat patties during refrigerated storage at a temperature of 4 ± 1 ℃. The analysis conducted for PPP revealed favorable phenolic and flavonoid content along with antioxidant activity. Additionally, PPP exhibited promising physicochemical features, including dietary fiber, good water holding and oil holding capacity. Patties were developed after incorporating 2, 4, 6, and 8% PPP (PPP1, PPP2, PPP3 and PPP4) respectively. The inclusion of PPP in the patties, particularly in the sample containing 8% PPP, yielded a significant elevation (P < 0.05) in dietary fiber content from 0.56 to 5.86%, accompanied by improved stability of lipids and proteins against oxidation. The incorporation of PPP at a level of 6% (PPP3) resulted in increased sensory ratings and an overall improvement in the quality of the patties. The inclusion of 8% PPP in the samples demonstrated a reduction in the total plate count of the samples from 6.96 to 5.06 log CFU/g on the 21st day of the study. Scanning electron microscopy images showed that compared to the control patties, the PPP-treated patties had less surface roughness and uneven distribution. Therefore, the incorporation of PPP has been demonstrated to be a successful approach for enhancing the patties' storage stability and dietary fiber, which may have potential health benefits. [ABSTRACT FROM AUTHOR]

While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study's hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students' levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students' academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students' outcomes through ChatGPT usage. [ABSTRACT FROM AUTHOR]

Fagonia indica Burm.f. (1768) is a medicinally important plant showing diverse pharmaceutical benefits. It is renowned for its ability to biosynthesize several anticancer and anti-inflammatory metabolites. For the eco-friendly and sustainable synthesis of phytochemicals and plant biomass, a biotechnological technique, "elicitation," is a highly effective method in various in vitro cultures. The present study includes using various concentrations of Fusarium oxysporum Schlecht. as an elicitor in callus cultures of Fagonia indica. The main goal was to achieve enhancement in biomass production and secondary metabolism. The findings demonstrated that maximum biomass production (FW: 167.42 ± 3.99 g per 100 mL; DW: 12.53 ± 1.04 g per 100 mL) was observed at 50 mg L−1 of Fusarium oxysporum as compared to the control. Secondary metabolites showed immense production (phenolic content (9.68 ± 0.23 µg mg−1); flavonoid content (2.814808 ± 0.11 µg mg−1)) in callus cultures treated with 10 mg L−1 of Fusarium oxysporum as compared with control. Moreover, the cultures possessed the highest antioxidant capacity, as determined by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) radical cation based assay and α, α-diphenyl-β-picrylhydrazyl (DPPH) free radical scavenging assay, ((821.51 ± 3.20 µmol TEAC per mg DW of ABTS inhibition) (91% ± 1.45 of DPPH inhibition)) at 10 mg L−1 concentration of Fusarium oxysporum, and the maximum ferric ion reducing activity (219.29 ± 2.36 µmol TEAC per mg DW) was noticed at 1.0 mg L−1 concentration of F. oxysporum. Fagonia indica cultures also indicated the highest percent inhibition against cyclooxygenases (COX-1: 51.93% ± 1.74 and COX-2: 40.57% ± 1.99), lipoxygenase (15-LOX: 65.72% ± 1.44), and phospholipase A2 (sPLA2: 49.29% ± 1.75), when treated with different concentrations of F. oxysporum. HPLC analyses showed a significant accumulation of pharmacologically active components in the treated samples, with kaempferol (1245.56 mg g−1) and myricetin (1139.63 mg g−1) as the most accumulated compounds in the cultures with 10.0 mg L−1 concentration of Fusarium in contrast to the control. These findings revealed that in callus cultures of F. indica, F. oxysporum could boost biomass accumulation and secondary metabolite production. [ABSTRACT FROM AUTHOR]

This study assesses the mechanism of action of plant-based silver nanoparticles (AgNPs) against antibiotic-resistant bacteria. We compared AgNPs synthesized through Salvia moorcroftiana and Origanum vulgare extracts and their conjugates with the antibiotic Ceftriaxone for their capacity to cause oxidative damage through reactive oxygen species (ROS). We quantified ROS in the cells of two bacterial strains after treating them with all AgNP types and observed that AgNPs were most effective in K. pneumoniae as they resulted in the highest ChS1 count (44,675), while in P. aeruginosa, Cfx-AgNPs induced the highest levels of ROS with ChS1 count of 56,865. DNA analysis showed that both plant-based AgNPs (O-AgNPs = 0.192 and S-AgNPs = 0.152) were most effective in K. pneumoniae and S-AgNPs (abs = 0.174) and O-Cfx-AgNPs (abs = 0.261) in P. aeruginosa. We observed a significant increase in the levels of conjugated dienes (86.4 μM) and malondialdehyde (172.25 nM) in the bacterial strains after treatment with AgNPs, compared to the control (71.65 μM and 18.064 nM, respectively, in K. pneumoniae and P. aeruginosa). These results indicate lipid peroxidation. AgNPs also increased the levels of protein thiols (0.672 nM) compared to the control (0.441 nM) in K. pneumoniae, except for Chem-AgNPs (0.21 nM). These results suggest that plant-based AgNPs are more effective in oxidizing bacterial DNA, protein, and lipids than Chem-AgNPs. Furthermore, protein oxidation varied between AgNPs alone and AgNPs-antibiotic conjugates. The highest levels of protein thiols were found in the samples treated with O-Cfx-AgNPs (0.672 nM and 0.525 nM in K. pneumoniae and P. aeruginosa, respectively). The results demonstrated that AgNPs kill bacteria by altering bacterial macromolecules such as DNA, lipids, and proteins. [ABSTRACT FROM AUTHOR]

Antibiotic-resistant bacterial strains and the consequent surge in infections caused by them have become major public health concerns. Silver nanoparticles (AgNPs) exhibit antibacterial properties and have wide applications in biomedical sciences. In this study, AgNPs were synthesized in the presence of antibiotics: Ceftazidime (Cft), Cefotaxime (Cef), Ceftriaxone (Cfx), and Cefepime (Cpm), along with the extract of Mentha longifolia. Mentha longifolia-based AgNPs were kept as the control for all experiments. The associated metabolites, structural properties, surface charges, and antibacterial activity of the AgNPs were also evaluated. Overall, a blue-shift of SPR peaks was observed for control AgNPs (λmax = 421 nm, 422 nm, 426 nm, and 406 nm for Cft-AgNPs, Cef-AgNPs, Cfx-AgNPs, and Cpm-AgNPs, respectively), compared to the control (λmax = 438 nm). Fourier-transform infrared spectroscopy showed that antibiotic-based AgNPs had distinct peaks that corresponded to the respective antibiotics, which were not observed in the control. XRD analysis showed that there were observed changes in crystallinity in antibiotic-based AgNPs compared to the control. TEM images revealed that all samples had spherical nanoparticles with different sizes and distributions compared to the control. The Zeta potential for extract-based AgNPs was − 33.6 mV, compared to -19.6 mV for Cft-AgNPs, -2 mV for Cef-AgNPs, -21.1 mV for Cfx-AgNPs, and − 24.2 mV for Cpm-AgNPs. The increase in the PDI value for antibiotic-based AgNPs also showed a highly polydisperse distribution. However, the antibiotic-AgNPs conjugates showed significantly higher activity against pathogenic bacteria. The addition of antibiotics to AgNPs brought significant changes in structural properties and antibacterial activities. [ABSTRACT FROM AUTHOR]

Delphinium uncinatum Wall. (Ranunculaceae) is a native medicinal plant found on the sub-continent that is utilized to cure various human diseases. Using a total of three different tissue parts i.e., the stem, root, and mixed plant (leaves, stem, and root), methanolic extracts were prepared and evaluated to determine the best candidate for potential bioactivity. Phytochemical constituents in prepared extracts were quantified through high-performance liquid chromatography analysis that demonstrated the presence of four flavonoid compounds, rutoside, nicotiflorin, narcissoside, and violanthin in varying proportions. Strong antibacterial and antioxidant potency but mild α-amylase and cytotoxic potential was elucidated. Highest anti-inflammatory potential was found against PLA2 (36.6 ± 2.08) and 15-LOX (30.33 ± 1.52) followed by COX-1 (25.1 ± 2.02) and COX-2 (19.33 ± 1.52). Anti-aging potential showed strong inhibitory actions toward pentosidine advanced glycation end products (AGEs) and visperlysine AGEs; however, least inhibitory activities were observed against hyaluronidase and elastase. These findings made us aware of further detailed research to determine a novel drug. [ABSTRACT FROM AUTHOR]

Salinity, low temperature, and drought are major environmental factors in agriculture leading to reduced crop yield. Dehydrins (DHNs) are induced transcriptionally during cellular dehydration and accumulate in different tissues during abiotic stresses. Here we isolated and characterized a bacterial gene BG757 in Arabidopsis, encoding a putative dehydrin type protein. ABA induces the expression of various dehydrins in plants, therefore, to elucidate the potential role, ABA sensitivity was examined in Arabidopsis transgenic lines expressing BG757. Interestingly, BG757-expressing plants showed hypersensitivity towards NaCl and ABA during seed germination. In addition to germination, BG757-expressing plants also showed root growth retardation in the presence of ABA and NaCl when compared with wild type (WT), suggesting that BG757 positively regulate salt stress and ABA response. Furthermore, BG757-expressing plants showed significant drought tolerance compared with WT. Consistent with drought tolerance, expression levels of stress inducible genes (DREB2A, RD22, RD26, LEA7 and SOS1) were strongly upregulated in transgenic plants compared with WT. All together these results suggest that heterologous expression of bacterial gene, BG757 in plants promotes resistance to environmental stresses. [ABSTRACT FROM AUTHOR]

Manual assessment of biomedical imaging based diagnostics is limited as it is time-consuming and subjective. Bio-inspired diagnostics applications on embedded and mobile devices are becoming more popular as they overcome these limitations and aid in early detection and diagnosis. The neural theory of visual attention puts forward that the processing resources are dedicated to more important information, for resource saving and performance improvement. Moreover, adversarial learning can potentially alleviate the various biases of the human cognitive system. The limited performance of the current lightweight network approaches can be attributed to the absence of above-mentioned properties of the human cognition. Accordingly, we introduce GLAN, a lightweight attention based network that is particularly well suited to embedded and mobile devices. GLAN's design follows lightweight architecture design principles for encoder-decoder design. To improve performance a twofold novel strategy is adopted. Firstly, we equip the encoder and the decoder with lightweight attention mechanisms to increase their focus and improve segmentation performance. Secondly, adversarial learning is employed with augmentation to increase the generalization ability of the lightweight attention network. e have evaluated our GLAN on three different applications including lung segmentation, digestive tract polyp segmentation and optic disc segmentation. GLAN is quite competitive in terms of segmentation performance while comprehensively outperforming recent alternatives in terms of computational requirements. Specifically, GLAN requires 94.17%, 78.18%, 80.95% and 67.56% less computational parameters as compared with four recent lightweight alternatives. This advocates its application in real-time biomedical imaging diagnostics on embedded and mobile devices in clinical settings. [ABSTRACT FROM AUTHOR]

Nanofibers have gained significant consideration due to their unique structural properties and applicability in a wide range of sectors. This study aimed to develop polyacrylonitrile (PAN)/graphene co-electrospun nanofibers at different nanofiller loadings. The electrospinning conditions were prior optimized for potential difference, dope solution feed rate, and needle-collector distance to get bead-free electrospun fibers. The prepared composite nanofibers were characterized using various instrumental techniques. The results demonstrated that prepared composite electrospun were hydrophilic (water contact angle—WCA—7.9°) as compared to control PAN nanofibers (WCA: 135.9°). The nanofibers diameter of control PAN (611 nm) decreased to 515 nm after graphene loading under the same electrospinning conditions. The tensile strength of the control PAN fibers (2.95 MPa) increased to 4.37 MPa, whereas the respective elongation at the break decreased from 96 to 44%. The FTIR analysis expressed a strong bonding of cyanide functional groups in the PAN with the polar groups at the graphene nanoplates in the composite electrospun fibers. The finding of the study is the successful development of PAN/graphene composite electrospun nanofibers with tuned thermomechanical properties. [ABSTRACT FROM AUTHOR]

Recent progress in encoder–decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth or size to meet computer memory constraints. In this paper, we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our illustrative experiments, we use DeepLabV3+ (deep large-size), M2U-Net (deep lightweight), U-Net (shallow large-size), and U-Net Lite (shallow lightweight). Our results suggest that median frequency is the best complexity measure when deciding on an acceptable input downsampling factor and using a deep versus shallow, large-size versus lightweight network. For high-complexity datasets, a lightweight network running on the original images may yield better segmentation results than a large-size network running on downsampled images, whereas the opposite may be the case for low-complexity images. [ABSTRACT FROM AUTHOR]

Melatonin is a natural indolamine that regulates many physiological functions in plants. The most prominent role of melatonin in plants has been its ability to work as an anti-stressor agent. Exogenous melatonin can prevent cell death and promote cell proliferation through its antioxidant properties, enhancement of polyamine biosynthesis, and the ability to shift cell metabolism in case of stressors like sugar starvation. Melatonin scavenges reactive oxygen species and thus preventing damage to cell membranes and other organelles. Its application in different plant culture systems reveals its important physiological and biochemical roles during the growth and development of these cultures. It has been observed that the exogenous melatonin protects callus culture, reduces cold-induced apoptosis in cell suspension, and stimulates adventitious and lateral roots formation. This review presents the physiological and biochemical effects of exogenous melatonin on in vitro culture systems, including its impact on biomass accumulation, growth, and development of plants. [ABSTRACT FROM AUTHOR]

Silicon (Si) has not been accepted as an essential element; although its beneficial role in plants against abiotic and biotic stresses is well known. In this regard an experiment was designed to optimize suitable Si dose among four different concentration (control, 100, 200 and 300 ppm) applied via rooting medium as soil treatment under saline (200 mM NaCl) and non-saline growing conditions in four barley genotypes (B-9006, B-15,002, Jou-87 and 14,003). It has been observed that imposition of salinity stress (200 mM NaCl) considerably reduced growth, photosynthetic pigments (Chl-a, Chl-b, total Chl. and carotenoids), leaf relative water contents, Si and K+ contents while Na+ and total soluble protein contents were remarkably enhanced. Alternatively, the rooting application of different concentrations of Si improved growth by enhancing synthesis of photosynthetic pigments and increasing K+ contents while reducing Na+ constituents in roots and leaves of barley plants grown under salinity stress. Furthermore, a negative correlation analysis was found among leaf and root Si contents with leaf and root Na+ contents among barley genotypes under salinity stress. The results also revealed that among all Si levels, 200 ppm Si dose proved to be the best for amelioration of salinity (200 mM NaCl) stress in barley crop. [ABSTRACT FROM AUTHOR]

The callus cultures of Fagonia indica could prove as factories for the production of important phytochemicals when triggered through different types of stress. In this study, we initiated callus cultures from healthy stem explants in the presence of iron-doped zinc oxide nanoparticles (Fe-ZnO-NPs). We performed experiments with the callus cultures of F. indica to determine the impact of Fe-ZnO-NPs in concentrations (15.62–250 µg/mL) on biomass accumulation, production of important phenolic and flavonoids, and antioxidative potential. Our results showed that maximum callus biomass [Fresh weight (FW) = 13.6 g and Dry weight (DW) = 0.58 ± 0.01] was produced on day 40 when the media was supplemented with 250 μg/mL Fe-ZnO-NPs. Similarly, maximum total phenolic content (268.36 μg GAE/g of DW) was observed in 40 days old callus added with 125 μg/mL Fe-ZnO-NPs. Maximum total flavonoid content (78.56 μg QE/g of DW) was recorded in 20 days old callus grown in 62.5 μg/mL Fe-ZnO-NPs containing media. Maximum total antioxidant capacity (390.74 µg AAE/g of DW) was recorded in 40 days old callus with 125 μg/mL Fe-ZnO-NPs treated cultures, respectively. Similarly, the highest free radical scavenging activity (93.02%) was observed in callus derived from media having 15.62 µg/mL Fe-ZnO-NPs. The antioxidant potential was observed to have positive correlation with TPC (r = 0.44). HPLC analysis showed that Fe-ZnO-NPs produced compounds (e.g., Epigallocatechin gallate) that were either absent or in lesser quantities in the control group. These results showed that Fe-ZnO-NPs elicitors could increase the biomass and activate secondary metabolism in F. indica cells. Key Message: Nanomaterial supplementation engineers the cells in an effective way to trigger biomass and secondary metabolism for the production of important phenolic compounds in plant cells. [ABSTRACT FROM AUTHOR]

Protection of crop plants from phytopathogens through endophytic bacteria is a newly emerged area of biocontrol. In this study, endophytic bacteria were isolated from the rhizosphere of Cannabis sativa. Based on initial antimicrobial screening, three (03) bacteria Serratia marcescens MOSEL-w2, Enterobacter cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 were selected. Antimicrobial assays of these selected bacteria against Phytophthora parasitica revealed that E. cloacae MOSEL-w7 and Paenibacillus sp. MOSEL-w13 possessed strong activity against P. parasitica. All these bacterial extracts showed strong inhibition against P. parasitica at different concentrations (4–400 µg mL−1). P. parasitica hyphae treated with ethyl acetate extract of E. cloacae MOSEL-w7 resulted in severe growth abnormalities compared to control. The extracts were further evaluated for in vivo detached-leaf assay against P. parasitica on the wild type tobacco. Application of 1% ethyl acetate bacterial extract of S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus sp. MOSEL-w13 reduced P. parasitica induced lesion sizes and lesion frequencies by 60–80%. HPLC based fractions of each extract also showed bioactivity against P. parasitica. A total of 24 compounds were found in the S. marcescens MOSEL-w2, 15 compounds in E. cloacae MOSEL-w7 and 20 compounds found in Paenibacillus sp. MOSEL-w13. LC–MS/MS analyses showed different bioactive compounds in the bacterial extracts such as Cotinine (alkylpyrrolidine), l-tryptophan, l-lysine, l-Dopa, and l-ornithine. These results suggest that S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 are a source of bioactive metabolites and could be used in combination with other biocontrol agents, with other modes of action for controlling diseases caused by Phytophthora in crops. They could be a clue for the broad-spectrum biopesticides for agriculturally significant crops. [ABSTRACT FROM AUTHOR]

The tensile strength of welded joints subjected to thermal stresses degrades with age. Tensile strength is generally assessed using tensile test, which is a destructive technique. This degradation measurement using the nondestructive testing (NDT) method has merit as specimens are intact even after the test. In situ NDT offers another significant advantage that testing of material can be performed without removing the sample from its service. Ultrasonic testing (UT) is a widely used NDT technique for material characterization and flaws detection. Motivated by this, we aim to investigate the tensile strength of mild steel post-weld heat-treated samples through advanced signal processing of the acquired ultrasonic signals through UT of mild steel heat-treated welded specimens. To this end, mild steel welded specimens were prepared for this study using electric arc welding with E6013 electrodes. After welding, specimens were heat-treated at different temperatures and then normalized. Ultrasonic signals were acquired using the pulse-echo technique on different samples, heat-treated at different temperatures, and later ultrasonic signal's attenuation was measured. The acquired UT signals were then processed using advance signal processing techniques i.e., fast Fourier transform (FFT) and power spectral density (PSD). Measured attenuation of UT signals and discriminatory features computed through the application of signal processing techniques on acquired UT signals then correlated with the specimens' tensile strength. Analysis of results showed that the tensile strength, FFT power of UT signals, and UT signals' PSD energy decreases as UT signal attenuation, increases with an increase in heat treatment temperature. Results also revealed that there exists a relationship between FFT signals strength, PSD energy, and UT signals attenuation with respect to heat treatment temperatures. This study will help weld quality inspectors to use in situ UT for predicting the tensile strength of mild steel welded specimens using the relationship established through the proposed scheme. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) and stroke have devastating consequences and are major global public health issues. For patients that require a cerebral decompression after suffering a TBI or stroke, a decompressive craniectomy (DC) is the most commonly performed operation. However, retrospective non-randomized studies suggest that a decompressive craniotomy (DCO; also known as hinge or floating craniotomy), where a bone flap is replaced but not rigidly fixed, has comparable outcomes to DC. The primary aim of this project was to understand the current extent of usage of DC and DCO for TBI and stroke worldwide. Method: A questionnaire was designed and disseminated globally via emailing lists and social media to practicing neurosurgeons between June and November 2019. Results: We received 208 responses from 60 countries [40 low- and middle-income countries (LMICs)]. DC is used more frequently than DCO, however, about one-quarter of respondents are using a DCO in more than 25% of their patients. The three top indications for a DCO were an acute subdural hematoma (ASDH) and a GCS of 9-12, ASDH with contusions and a GCS of 3-8, and ASDH with contusions and a GCS of 9-12. There were 8 DCO techniques used with the majority (60/125) loosely tying sutures to the bone flap. The majority (82%) stated that they were interested in collaborating on a randomized trial of DCO vs. DC. Conclusion: Our results show that DCO is a procedure carried out for TBI and stroke, especially in LMICs, and most commonly for an ASDH. The majority of the respondents were interested in collaborating on a is a future randomized trial. [ABSTRACT FROM AUTHOR]

This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were collected and analyzed for physicochemical parameters including heavy metals (HM). The mean concentrations of physicochemical parameters and HM were within the drinking water guideline values set by the World Health Organization (WHO 2011) except 34%, 60%, and 56% of copper (Cu), nickel (Ni), and lead (Pb), respectively. Pollution sources were identified by various multivariate statistical techniques including correlation analysis (CA) and principal component analysis (PCA) indicating different origins both naturally and anthropogenically. Results of the water quality index (WQI) ranged from 13.58 to 209 with an average value of 77 suggesting poor water quality for drinking and domestic purposes. The poor water quality was mainly related to high sodium (alkalinity) and salinity hazards showing > 27% and 20% water samples have poor alkalinity and salinity hazards, respectively. Hazard quotient (HQ) and hazard index (HI) were used to determine the health risk of HM in the study area. For water-related health risk, HQingestion, HQdermal, and HI values were > 1, indicating noncarcinogenic health risk (NCR) posed by these HM to the exposed population. [ABSTRACT FROM AUTHOR]

This paper presents a real-time iris segmentation technique that is well suited to a fast implementation on an FPGA. One major hurdle associated with iris segmentation techniques is the use of iterative processes that lead to expensive hardware implementations. To circumvent this, the proposed algorithm uses the sign image obtained from subtracting the background, along with morphological operators to localise the pupil. The outer boundary is located by first normalising a selected image region that contains the iris, and then using a first-order gradient operator. The proposed non-iterative algorithm is implemented on an FPGA. Four near infrared (NIR) iris public databases, namely: CASIA-IrisV3-Lamp, MMU v1.0, ND-IRIS-0405 and NIST ICE 2005, are used to test the proposed algorithm. The proposed method for iris segmentation and normalization gives much better accuracy than the existing state-of-the-art methods implemented on hardware. The proposed realisation requires about 45% fewer logic registers and 52% fewer logic elements than the existing state-of-the-art implementations. [ABSTRACT FROM AUTHOR]

Application of nanomaterials is becoming the most effective strategy of elicitation to produce a desirable level of plant biomass with complex medicinal compounds. This study was, designed to check the influence of commercial iron nanoparticles (FeNPs) on physical growth characteristics, antioxidant status and production of steviol glycosides of in vitro grown Stevia rebaudiana. Results indicated that lower concentrations of FeNPs (45 µg/L) had a positive influence on morphological growth parameters. At a higher dose (90, and 135 µg/L) FeNPs in culture media were found detrimental to growth characteristics and development. Furthermore, the stress caused by FeNPs at 135 µg/L in cultures produced higher levels of total phenolic content (3.2 ± 0.042 mg/g dry weight: DW), total flavonoid content (1.6 ± 0.022 mg/g DW and antioxidant activity (73 ± 4.6%). In addition, plants grown in the presence of FeNPs at 90 µg/L resulted in higher enzymatic antioxidant activities (SOD = 3.2 ± 0.042 U/mg; POD = 2.1 ± 0.026 U/mg; CAT = 2.6 ± 0.034 U/mg and APx = 3.3 ± 0.043 U/mg), respectively. Furthermore, exposure to a low dose of FeNPs (45 µg/L) exhibited the maximum amount of stevioside (stevioside: 4.2 ± 0.058 mg/g (DW) and rebaudioside A: 4.9 ± 0.068 mg/g DW) as compared to high doses. The current investigation confirms the effectiveness of FeNPs in growth media and offers a suitable prospect for commercially desirable production of S. rebaudiana biomass with higher sweet glycosides profiles in vitro. Key message: Lower concentrations of Iron nano particles (FeNPs) had a positive influence on morphological growth parameters, production of antioxidant secondary metabolites and natural calorie free steviol glycosides in Stevia rebaudiana. [ABSTRACT FROM AUTHOR]

Fouling of heat exchangers in energy and power plant industry, oil and gas industry, pharmaceutical and food process industry, industrial waste heat recovery is a major problem which needs to be solved urgently. In the United Arab Emirates, a majority of oil & gas processing plants are located in the desert where water accessibility is limited, thus fin-fan type of heat exchangers are used for cooling and/or heating applications. Such harsh environments cause external fouling which becomes an unavoidable problem in these heat exchangers. With increased humidity and high process temperatures, the dust particulates start to stick on the outer surface of heat exchangers which drastically reduces the thermal performance and increases power consumption. In order to identify possible methods to control and/or eliminate such kinds of external fouling, it is important to identify the composition and morphology of such foulants. This study presents the characterization of selected fouling samples collected from several fin-fan type air cooled heat exchangers taken from a gas processing plant. Scanning electron microscope (SEM) tests were carried out to determine the physical characteristics of sample foulant. Variability in sizes and shapes was found between samples due, perhaps, to different working temperature ranges of the selected heat exchangers. X-ray diffraction (XRD) analyses revealed presence of quartz, calcite, and alumina with traces of halite and hematite. Laser flash analysis (LFA) was conducted to quantify the specific heat capacity, thermal diffusivity and thermal conductivity of the fouling samples. The diversity of these fouling samples reflects complexity with respect to their potential removal and effects on heat transfer characteristics. Though, this research work is a case study for a specific petrochemical plant, however, findings in this study can be adopted by other industries involved in similar chemical processing in the Middle East as well as other countries having similar weather and geological conditions. [ABSTRACT FROM AUTHOR]

Biotechnological strategies are needed to produce larger quantities of biomass and phytochemicals. In this study, callus cultures of Fagonia indica were elicited with different concentrations of chemically and biologically synthesized silver nanoparticles (chem- and bioAgNPs) to compare their effects on biomass, total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity of the extracts from callus. The results revealed that bioAgNPs being more biocompatible produced the highest biomass initially on day 10 (FW = 4.2152 ± 0.13 g; DW = 0.18527 ± 0.01 g) and day 20 (FW = 7.6558 ± 0.10 g; DW = 0.3489 ± 0.01 g) when supplemented in media as 62.5 µg/mL and 250 µg/mL, respectively. Initially, the highest TPC (319.32 ± 8.28 µg GAE/g of DW) was recorded on day 20 in chemAgNPs (31.25 µg/mL) induced callus as compared to TPC = 302.85 ± 3.002 µg GAE/g of DW in bioAgNPs-induced callus. Compared to the highest values of TFC (108.15 ± 2.10 µg QE/g of DW) produced in 15.6 µg/mL chemAgNPs-induced callus on day 20, TFC produced in bioAgNPs (62.5 µg/mL) was 168.61 ± 3.17 µg GAE/g of DW on day 10. Similarly, chemAgNPs-induced callus (62.5 µg/mL) showed the highest free radical scavenging activity (FRSA) i.e. 87.18% on day 20 while bioAgNPs (125 µg/mL) showed 81.69% FRSA on day 20 compared to highest among control callus (63.98% on day 40). The highest total antioxidant capacity of chemAgNPs-(125 µg/mL) induced callus was 330.42 ± 13.65 µg AAE/g of DW on day 20 compared to bioAgNPs-(62.5 µg/mL) induced callus (312.96 ± 1.73 µg AAE/g of DW) on day 10. Conclusively, bioAgNPs are potent elicitors of callus cultures of F. indica. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus (DM) is a major health problem, with a high prevalence worldwide, including in Pakistan. Insulin is a main treatment of type 1 DM (T1D) and type 2 DM (T2D). Many challenges and complications are linked with insulin self-administration (ISA). Methods: A cross-sectional survey was conducted at a single teaching hospital in Pakistan. Study participants were adults with T1D or T2D treated with insulin who attended the hospital. Data were collected using a pretested questionnaire that determined the participants' knowledge of DM and ISA and their attitudes towards ISA. Mann–Whitney tests and Kruskal–Wallis tests were applied to check the association between demographic characteristics and knowledge and attitude. The Spearman rho correlation test was used to check the correlation between knowledge and attitude scores. Data were analyzed by applying SPSS version 24.0. Results: Of the 105 survey participants, 66.9% were male and 33.1% were female, with a median age of 45 years. Only 22.9% of the participants had good knowledge about DM, 50.5% had average knowledge, and 26.7% had poor knowledge. The attitude towards ISA was favorable in 30.5% of participants, but unfavorable in 69.5%. Age and duration of insulin use were significantly related to knowledge and attitude scores, and there was positive linear correlation between knowledge score and attitude score. Conclusion: Overall, participants had poor knowledge and unfavorable attitudes regarding DM and ISA therapy. The positive linear correlation between knowledge and attitude score suggests that if patients are knowledgeable about DM, their attitude regarding DM and ISA will also be favorable. [ABSTRACT FROM AUTHOR]

Background: The safety and proper utilization of a drug depends not only on its pharmacological action, but also its interactions with other drugs and their metabolites. Therefore, a multifaceted biochemical system must be considered when prescribing any drug. Objective: The purpose of the present study is to identify potential drug–drug interactions (pDDIs) and investigate their level of severity, onset of action, mechanism of action, and incidence in the medical wards of Benazir Bhutto Shaheed Teaching Hospital, Abbottabad, Pakistan. Methods: A prospective cross-sectional study was conducted from November 2016 to January 2017 in the medical wards of a teaching hospital. A systematic random sampling technique was used to select patients from the available settings with one in every seven patients selected to provide a sample of 384 patients. Results: A total of 388 pDDIs were identified, which comprised 86 types of drug combinations. Across all pDDIs, most were of major severity (39.9%), followed by moderate (39.6%) and minor (19.5%) severity and contraindicated (0.7%). A majority of the study participants were male (292; 76%) and the mean age of the patients was 50 years. Overall, 51.0% patients had at least one pDDI. Conclusion: The overall prevalence of pDDIs is very high in medical wards, and the incidence of pDDIs is primarily associated with polypharmacy and patient age. [ABSTRACT FROM AUTHOR]

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier's performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier's performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation. [ABSTRACT FROM AUTHOR]

Computer-aided optic disk (OD) detection and segmentation is at the heart of modern fundus image screening systems for early detection and diagnosis of glaucoma and diabetic retinopathy. Algorithms that generalize well on fundus images with diseases, as well as screening images, are of utmost importance. This paper presents a method based on OD homogenization and subsequent contour estimation to address the challenges of OD detection in cases where either the OD boundary is discontinuous or very smooth, due to the presence of disease. This is achieved by local Laplacian filtering-based inpainting of the major vascular structure to complete the OD boundary and gradient-independent active contour estimation for unconstrained OD boundary detection. Experimental evaluation of the proposed method on three benchmark datasets and quantitative comparison with the best performing state-of-the-art methods in terms of four quantitative measures demonstrate its competitive performance and reliability for OD screening. [ABSTRACT FROM AUTHOR]

Many chronic eye diseases can be conveniently investigated by observing structural changes in retinal blood vessel diameters. However, detecting changes in an accurate manner in face of interfering pathologies is a challenging task. The task is generally performed through an automatic computerized process. The literature shows that powerful methods have already been proposed to identify vessels in retinal images. Though a significant progress has been achieved toward methods to separate blood vessels from the uneven background, the methods still lack the necessary sensitivity to segment fine vessels. Recently, a multi-scale line-detector method proved its worth in segmenting thin vessels. This paper presents modifications to boost the sensitivity of this multi-scale line detector. First, a varying window size with line-detector mask is suggested to detect small vessels. Second, external orientations are fed to steer the multi-scale line detectors into alignment with flow directions. Third, optimal weights are suggested for weighted linear combinations of individual line-detector responses. Fourth, instead of using one global threshold, a hysteresis threshold is proposed to find a connected vessel tree. The overall impact of these modifications is a large improvement in noise removal capability of the conventional multi-scale line-detector method while finding more of the thin vessels. The contrast-sensitive steps are validated using a publicly available database and show considerable promise for the suggested strategy. [ABSTRACT FROM AUTHOR]

Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks. [ABSTRACT FROM AUTHOR]

The correlation between retinal vessel structural changes and the progression of diseases such as diabetes, hypertension, and cardiovascular problems has been the subject of several large-scale clinical studies. However, detecting structural changes in retinal vessels in a sufficiently fast and accurate manner, in the face of interfering pathologies, is a challenging task. This significantly limits the application of these studies to clinical practice. Though monumental work has already been proposed to extract vessels in retinal images, they mostly lack necessary sensitivity to pick low-contrast vessels. This paper presents a couple of contrast-sensitive measures to boost the sensitivity of existing retinal vessel segmentation algorithms. Firstly, a contrast normalization procedure for the vascular structure is adapted to lift low-contrast vessels to make them at par in comparison with their high-contrast counterparts. The second measure is to apply a scale-normalized detector that captures vessels regardless of their sizes. Thirdly, a flood-filled reconstruction strategy is adopted to get binary output. The process needs initialization with properly located seeds, generated here by another contrast-sensitive detector called isophote curvature. The final sensitivity boosting measure is an adoption process of binary fusion of two entirely different binary outputs due to two different illumination correction mechanism employed in the earlier processing stages. This results in improving the noise removal capability while picking low-contrast vessels. The contrast-sensitive steps are validated on a publicly available database, which shows considerable promise in the strategy adopted in this research work. [ABSTRACT FROM AUTHOR]

The enhancement of coherent flow-like structures is desired for many image processing tasks, such as segmentation and feature detection. This task can be accomplished in a natural way by adopting anisotropic diffusion filtering using a diffusion matrix adapted to the local structure. This method is referred to as coherence-enhancing diffusion (CED). The performance of CED can be analyzed by observing the evolution of the orientation field (OF) associated with an evolving diffusion matrix. It was revealed from a series of experiments that the final OF from a CED-enhanced image sometimes strays from its true underlying OF (marked by a human expert), degrading its performance. In this paper, a strategy is proposed which repeatedly cleans the OF associated with a diffusion matrix. Thus, for every iteration of CED, its OF is diffused separately until it converges and is then fed back to the CED process to move forward. This hypothesis is tested with the motive of getting an enhanced CED performance. The proposed scheme is validated using fingerprint data, and their numerical results are displayed. [ABSTRACT FROM AUTHOR]

Anisotropic diffusion is an important noise reduction process in many pattern recognition systems. The local orientations and shape are encoded in a descriptor, called second-moment matrix, to be used as the central part of the process. For a noisy image, it becomes hard to find the elements of the matrix with reasonable accuracy. Two layers of subsequent Gaussian smoothing are applied conventionally: One is referred to as noise scale and the other integration scale. It is still not guaranteed that a second-moment matrix is correctly aligned with underlying pattern flow directions. It is largely believed that the integration scale handles this orientation discrepancy. Therefore, some researchers suggested a space-dependent integration scale strategy. We propose here a calibration strategy using directional filterbank (DFB). The second-moment matrix is rotated to go along the proper ridge directions with an angle correction term from DFB. The experiments conducted show promise for the calibrated anisotropic diffusion process in terms of improved recognition rate over that of the un-calibrated process. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation plays a major role in the detection of many eye diseases, and it is required to implement an automated algorithm for analyzing the progress of eye diseases. A variety of automated segmentation methods have been presented but almost all studies to date showed weakness in their low sensitivity toward narrow low-contrast vessels. A new segmentation method is proposed to address the issue of low sensitivity, by including modules such as principal component analysis-based color-to-gray conversion, scale normalization factors for improved narrow vessel detection, anisotropic diffusion filtering with an adequate stopping rule, and edge pixel-based hysteresis threshold. The impact of these additional steps is assessed on publicly available databases like DRIVE and STARE. For the case of DRIVE database, the sensitivity is raised from 73 to 75%, while maintaining the accuracy of 96.5%, and found to provide evidence of improved sensitivity. [ABSTRACT FROM AUTHOR]

Eye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions. [ABSTRACT FROM AUTHOR]

Cisternostomy for traumatic brain injury - rigorous evaluation is necessary That paper reported the rationale around cisternal opening in TBI and also presented the results of a single-surgeon series of patients treated with decompressive craniectomy (DC) plus cisternostomy (272 patients) and cisternostomy alone (476 patients). Early neuroprotective bifrontal DC for mild to moderate intracranial hypertension is not superior to medical management for patients with diffuse TBI [[4]] 20 van Leeuwen N, Lingsma HF, Perel P. Prognostic value of major extracranial injury in traumatic brain injury: an individual patient data meta-analysis in 39,274 patients. [Extracted from the article]

Ajuga bracteosa is a medicinally important plant globally used in the folk medicine against many serious ailments. In the present study, effects of two significant elicitors, methyl jasmonate (Me-J) and phenyl acetic acid (PAA) were studied on growth parameters, secondary metabolites production, and antioxidant potential in adventitious root suspension cultures of A. bracteosa. The results showed a substantial increase in biomass accumulation, exhibiting longer log phases of cultures growth in response to elicitor treatments, in comparison to control. Maximum dry biomass formation (8.88 DW g/L) was recorded on 32nd day in log phase of culture when 0.6 mg/L Me-J was applied; however, PAA at 1.2 mg/L produced maximum biomass (8.24 DW g/L) on day 40 of culture. Furthermore, we observed the elicitors-induced enhancement in phenolic content (total phenolic content), flavonoid content (total flavonoid content) and antioxidant activity (free radical scavenging activity) in root suspension cultures of A. bracteosa. Application of 0.6 mg/L and 1.2 mg/L of Me-J, root cultures accumulated higher TPC levels (3.6 mg GAE/g DW) and (3.7 mg GAE/g DW) in the log phase and stationary phase, respectively, while 2.5 mg/L Me-J produced lower levels (1.4 mg GAE/g DW) in stationary phase of growth stages. Moreover, TFC and FRSA values were found in correspondence to TPC values in the respective growth phases at the similar elicitor treatment. Thus, a feasible protocol for establishment of adventitious roots in A. bracteosa was developed and enhancement in biomass and metabolite content in adventitious root was promoted through elicitation. [ABSTRACT FROM AUTHOR]

Fagonia indica, a very important anticancer plant, has been less explored for its in vitro potential. This is the first report on thidiazuron (TDZ)-mediated callogenesis and elicitation of commercially important phenolic compounds. Among the five different plant growth regulators tested, TDZ induced comparatively higher fresh biomass, 51.0 g/100 mL and 40.50 g/100 mL for stem and leaf explants, respectively, after 6 weeks of culture time. Maximum total phenolic content (202.8 μg gallic acid equivalent [GAE]/mL for stem-derived callus and 161.3 μg GAE/mL for leaf-derived callus) and total flavonoid content (191.03 μg quercetin equivalent [QE]/mL for stem-derived callus and 164.83 μg QE/mL for leaf-derived callus) were observed in the optimized callus cultures. The high-performance liquid chromatography (HPLC) data indicated higher amounts of commercially important anticancer secondary metabolites such as gallic acid (125.10 ± 5.01 μg/mL), myricetin (32.5 ± 2.05 μg/mL), caffeic acid (12.5 ± 0.52 μg/mL), catechin (9.4 ± 1.2 μg/mL), and apigenin (3.8 ± 0.45 μg/mL). Owing to the greater phenolic content, a better 2-2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging activity (69.45 % for stem explant and 63.68 % for leaf explant) was observed in optimized calluses. The unusually higher biomass and the enhanced amount of phenolic compounds as a result of lower amounts of TDZ highlight the importance of this multipotent hormone as elicitor in callus cultures of F. indica. [ABSTRACT FROM AUTHOR]

Images can be broadly classified into two types: isotropic and anisotropic. Isotropic images contain largely rounded objects while anisotropics are made of flow-like structures. Regardless of the types, the acquisition process introduces noise. A standard approach is to use diffusion for image smoothing. Based on the category, either isotropic or anisotropic diffusion can be used. Fundamentally, diffusion process is an iterated one, starting with a poor quality image, and converging to a completely blurred mean-value image, with no significant structure left. Though the process starts by doing a desirable job of cleaning noise and filling gaps, called under-smoothing, it quickly passes into an over-smoothing phase where it starts destroying the important structure. One relevant concern is to find the boundary between the under-smoothing and over-smoothing regions. The spatial entropy change is found to be one such measure that may be helpful in providing important clues to describe that boundary, and thus provides a reasonable stopping rule for isotropic as well as anisotropic diffusion. Numerical experiments with real fingerprint data confirm the role of entropy-change in identification of a reasonable stopping point where most of the noise is diminished and blurring is just started. The proposed criterion is directly related to the blurring phenomena that is an increasing function of diffusion process. The proposed scheme is evaluated with the help of synthetic as well as the real images and compared with other state-of-the-art schemes using a qualitative measure. Diffusions of some challenging low-quality images from FVC2004 are also analyzed to provide a reasonable stopping rule using the proposed stopping rule. [ABSTRACT FROM AUTHOR]

Synthesis of silver nanoparticles by plants and plant extracts (green synthesis) has been developed into an important innovative biotechnology, especially in the application of such particles in the control of pathogenic bacteria. This is a safer technology, biologically and environmentally, than synthesis of silver nanoparticles by chemical or physical methods. Plants are preferable to microbes as agents for the synthesis of silver nanoparticles because plants do not need to be maintained in cell culture. The antibacterial activity of bionanoparticles has been extensively explored during the past decade. This review examines studies published in the last decade that deal with the synthesis of silver nanoparticles in plants and their antibacterial activity. [ABSTRACT FROM AUTHOR]

Light plays a pertinent role in plant photo-morphogenesis and it is believed to have an impact on the melatonin-induced physiological functions. In the current study, different light regimes were employed with varying levels of melatonin, either singly or in combination with auxins for the growth and development of adventitious roots in Withania somnifera L. It was observed that 600 µM melatonin favored maximum adventitious root induction frequency (58 %) in cultures incubated under continuous dark conditions. However, adequate root growth (number and length of roots) was observed under 16 h light/8 h dark at 600 µM melatonin. Nevertheless, the interactive effect of light and melatonin was found stimulating for profound production of commercially important secondary metabolites. Correlation among growth parameters and biochemical markers was also observed in the current report. Data on total phenolic content and total flavonoid content were found at higher coincidence with each other and with DPPH antioxidant activity. In conclusion, exogenously applied melatonin mimics IAA activity in root growth and regulates well in 16-h light/8-h dark, thereby giving protection to plant system against light stress. [ABSTRACT FROM AUTHOR]

The inter-annual variation and linear trends of the surface air temperature in the regions in and around the Bay of Bengal have been studied using the time series data of monthly and annual mean temperature for 20–40 years period within 1951–1990. The study area extends from Pusma Camp of Nepal in the north and Kuala Lumpur of Malaysia in the south and between 80--100 ° E. The annual variation of temperature has also been studied using the mean monthly temperature for the variable time frames 1961–1975, 1976–1990 and 1961–1990. The trend of temperature has been analyzed using linear regression technique with the data from 1961–1990, which showed that the warming trend is dominant over the study areas except for a few stations. It has been found that Nepal shows predominant warming trends. Bangladesh and the adjacent areas of India and the northern part of Bay of Bengal adjacent to the Bangladesh coast have shown strong warming trends of the annual temperature with maximum at Dhaka (0.037 °C/year). The near equatorial zone, i.e., southern India, Sri Lanka and part of Thailand and Malaysia (Kuala Lumpur) shows warming trends in the annual mean temperature with strong warming at Pamban and Anuradhapura (around 0.04 °C/year). The cooling trends have been observed at a few stations including Port Blair, Yangoon and Cuttack. Further analysis shows the presence of prominent ENSO scale of variations with time period 4–7 years and 2–3 years for almost all the stations. The decadal mode with T >7 years is present in some data series. The results of the variations of temperature with respect to the Southern Oscillation Index (SOI) show that SOI has some negative correlation with temperature for most of the stations except those in the extreme northeast. It has been found that positive anomaly of temperature has been observed for El Niño events and negative anomaly for the La Nina events. [ABSTRACT FROM AUTHOR]

The sea surface temperature (SST) variations play a veryimportant role in the genesis and maintenance of meteorological and oceanographic processessuch as monsoon depressions and subsequent floods, large-scale sea level fluctuationsand genesis of tropical cyclones. Many low lying coastal regions of South Asia are adjacentto river deltas and have large population. The dense population, poor economy and severalother socio-economic factors make these areas most vulnerable to the impact of climate change. Variability of sea surface temperature (SST) is importantas the duration and intensity of SST provide the basis for studies related to climatic changescenario. In this study an attempt has been made to estimate the recent SST trends in the coastalwaters of some cities, which lie on the Arabian Sea and Bay of Bengal. The annual andinterannual variability has also been studied. The SST variations have then been linkedwith the El Nino and La Nina events. The NOAA-NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST fields from 1985-1998, created in the Jet Propulsion Laboratory(JPL), USA are used in this study. Here the quality of data is an important factor toobtain reliable estimates of Sea Surface Temperature (SST) trends and other related parameters.However, this is not possible with the conventional type data, due to low quality as wellas sparse data in the region. Though the satellite based SST climatologies have shorterobservation lengths, they can provide reliable estimates of recent SST variability overa large oceanic areas with sparse or no data. Increasing trend of SST is observed throughout all theseasons in the northern Arabian Sea extending from Oman to Karachi and Mumbai and furthersouth to Salalah and Colombo. However, in coastal islands stations further south ofIndia such as at Colombo the increment is not significant. Though the increasing trend in SSTduring winter is not significant, nevertheless it shows the increasing influence of coldspells on this Island. An interesting situation has been observed in the Bay of Bengal. On anaverage, increasing trends in the annual SST were observed in Visakhaputnam. But at thestations located in the northeastern part of Bay of Bengal, namely Hiron Point and Cox'sBazar reverse conditions are observed. In the Southern Bay of Bengal variations in SST isnot significant which reflects in the SST analysis of Chennai and Port Blair stations. Locationof these stations at lower latitudes (near by equator) probably is the reason for this insignificantchange. It has been found that the interannual mode of SST variations dominate the linear SSTtrends which is characterized by the El Nino Southern Oscillations (ENSO) scale cycle. [ABSTRACT FROM AUTHOR]

Lakes of western Victoria are typically shallow (2–3 m), slightly saline (3–8 ppt) and eutrophic (total phosphorus 0.1–5 mg l-1 and total nitrogen 3–8 mg l-1). These lakes are prone to algal blooms in late spring and summer. We conducted experiments in ponds to determine whether lake zooplankton exert enough grazing pressure to control algal blooms and also if carp predation can regulate zooplankton communities and increase algal biomass. Zooplankton grazing (high zooplankton, low zooplankton and control) and carp predation (carp, nutrient and control) experiments had three treatments and each treatment had three replicates. Algal numbers and biomass (28 μg l-1) were uniform in all ponds before zooplankton stocking. When stocked with lake zooplankton, algae were consumed efficiently and after 30 days algal numbers and biomass were significantly lower than those in control ponds (0.4 μg l-1 and 33 μg l-1, respectively). Ponds stocked with low zooplankton numbers were intermediate. Complete elimination of Cladocera and subsequent increase in algal biomass (from 4 to 45 μg l-1) occurred after 30 days in carp treatment ponds. Zooplankton and algal populations were unaffected when nutrients but no carp were added and also in control ponds with no added nutrients or carp. The pond experiments illustrated the mechanics of a carp mediated trophic cascade when stocked with species and densities of zooplankton naturally occurring in lakes of western Victoria. Confirmation of whether this process occurs in turbid Australian lakes is urgently required. [ABSTRACT FROM AUTHOR]