Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for ...Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.
Drought stress affects plant growth and development by altering physiological and biochemical processes resulting in reduced crop productivity. Zinc (Zn) is an essential micronutrient that plays ...fundamental roles in crop resistance against the drought stress by regulating various physiological and molecular mechanisms. Under drought stress, Zn application improves seed germination, plant water relations, cell membrane stability, osmolyte accumulation, stomatal regulation, water use efficiency and photosynthesis, thus resulting in significantly better plant performance. Moreover, Zn interacts with plant hormones, increases the expression of stress proteins and stimulates the antioxidant enzymes for counteracting drought effects. To better appraise the potential benefits arising from optimum Zn nutrition, in the present review we discuss the role of Zn in plants under drought stress. Our aim is to provide a complete, updated picture in order to orientate future research directions on this topic.
Sustainable Development Goals (SDGs) highlight the importance of poverty reduction, and call for policy implementation that leads to the socio-economic development of impoverished people. However, ...there is a lack of knowledge about assessing individual-level socio-economic development, and how financial inclusion through microfinance can contribute to it. Therefore, the role of commercially operated Microfinance Banks (MFBs) is also considered to be controversial in the literature. This study assesses the overall socio-economic development by considering different sustainable livelihoods, multidimensional poverty, living standards, and social development measures. Thus, the Multidimensional Poverty Index (MPI), and Living Standard Index (LSI) have been estimated to gauge poverty and improvements in living standards. Data comprising 503 customers of MFBs, and 500 control respondents, has been gathered through a survey to signify this impact for two years. This paper substantiates that the microfinance obtained from MFBs contributes positively towards sustainable livelihoods, multidimensional poverty reduction, and living standards. However, microfinance does not contribute to social development. Impoverished people, mainly women living in urban areas, reap more benefits from microfinance, than their rural counterparts. Overall, financial inclusion shall be a gateway to achieve the SDGs in the long run through the socio-economic development of an impoverished segment of the society.
Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified ...technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms ...huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient's survivor prediction. To predict patient's survival, this study employs nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient's survival.
Cell-free DNA (cfDNA) refers to short fragments of acellular nucleic acids detectable in almost all body fluids, including blood, and is involved in various physiological and pathological phenomena ...such as immunity, coagulation, aging, and cancer. In cancer patients, a fraction of hematogenous cfDNA originates from tumors, termed circulating tumor DNA (ctDNA), and may carry the same mutations and genetic alterations as those of a primary tumor. Thus, ctDNA potentially provides an opportunity for noninvasive assessment of cancer. Recent advances in ctDNA analysis methods will potentially lead to the development of a liquid biopsy tool for the diagnosis, prognosis, therapy response monitoring, and tracking the rise of new mutant sub-clones in cancer patients. Over the past few decades, cancer-specific mutations in ctDNA have been detected using a variety of untargeted methods such as digital karyotyping, personalized analysis of rearranged ends (PARE), whole-genome sequencing of ctDNA, and targeted approaches such as conventional and digital PCR-based methods and deep sequencing-based technologies. More recently, several chip-based electrochemical sensors have been developed for the analysis of ctDNA in patient samples. This paper aims to comprehensively review the diagnostic, prognostic, and predictive potential of ctDNA as a minimally invasive liquid biopsy for cancer patients. We also present an overview of current advances in the analytical sensitivity and accuracy of ctDNA analysis methods as well as biological and technical challenges, which need to be resolved for the integration of ctDNA analysis into routine clinical practice.
Opportunities and challenges in translational application of ctDNA along with recent developments in chip-based ctDNA detection technologies have been reviewed.
In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is ...routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) outbreak originating in Wuhan, China, has raised global health concerns and the pandemic has now been reported on all ...inhabited continents. Hitherto, no antiviral drug is available to combat this viral outbreak.
Keeping in mind the urgency of the situation, the current study was designed to devise new strategies for drug discovery and/or repositioning against SARS-CoV-2. In the current study, RNA-dependent RNA polymerase (RdRp), which regulates viral replication, is proposed as a potential therapeutic target to inhibit viral infection.
Evolutionary studies of whole-genome sequences of SARS-CoV-2 represent high similarity (> 90%) with other SARS viruses. Targeting the RdRp active sites, ASP760 and ASP761, by antiviral drugs could be a potential therapeutic option for inhibition of coronavirus RdRp, and thus viral replication. Target-based virtual screening and molecular docking results show that the antiviral Galidesivir and its structurally similar compounds have shown promise against SARS-CoV-2.
The anti-polymerase drugs predicted here-CID123624208 and CID11687749-may be considered for in vitro and in vivo clinical trials.
Irrigation using sewage water can be beneficial, as it can increase the productivity of crops but has negative consequences on crops, soil contamination, and human health. It contains a variety of ...toxins, such as chemicals and heavy metals, which damage the soil and crops. In this regard, the aim of the research was to assess the potential health hazards of iron (Fe) metal in food crops (leafy and root crops) treated with wastewater (T_1), canal water (T_2), and tube well water (T_3). Water, soil, and edible components of food crops were collected at random from three distinct locations. Fe concentration in samples was estimated using atomic absorption spectrophotometer, following wet digestion method. The Fe concentrations, ranged from 0.408 to 1.03 mg/l in water, 31.55 to 187.47 mgkg.sup.-1 in soil and 4.09 to 32.583 mgkg.sup.-1 in crop samples; which were within permissible limits of the World Health Organization (WHO). There was a positive correlation between soils and crops. The bioconcentration factor, enrichment factor (EF), daily intake of metals (DIM), health risk index (HRI), and target hazard quotient (THQ) all values were 1, which indicated soil contamination, but there was no Fe toxicity in crops, no health risk, and no-carcinogenic risk for these food crops in humans. To prevent the excessive accumulation of Fe metal in the food chain, regular monitoring is needed.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
With revolutionary advances in next-generation sequencing, the human transcriptome has been comprehensively interrogated. These discoveries have highlighted the emerging functional and regulatory ...roles of a large fraction of RNAs suggesting the potential they might hold as stable and minimally invasive disease biomarkers. Although a plethora of molecular-biology- and biosensor-based RNA-detection strategies have been developed, clinical application of most of these is yet to be realized. Multifunctional nanomaterials coupled with sensitive and robust electrochemical readouts may prove useful in these applications. Here, we summarize the major contributions of engineered nanomaterials-based electrochemical biosensing strategies for the analysis of miRNAs. With special emphasis on nanostructure-based detection, this review also chronicles the needs and challenges of miRNA detection and provides a future perspective on the presented strategies.
miRNAs play crucial roles in different aspects of many diseases including cancers, suggesting that miRNAs could potentially serve as cancer biomarkers.
Considerations that need to be measured to triumph the full functionality of the miRNAs as diagnostics and prognostics markers are discussed.
Grand challenges associated with the current miRNA detection strategies are addressed.
Multifunctional activity (i.e., porous architecture, magnetic property, enzyme-like activity, conductivity and biocompatibility) of engineered nanomaterials is the key to the functionality and simplicity of many current electrochemical miRNA biosensors.
Using the concept of porous architectures, a new class of ferric-oxide-based nanomaterials has recently been developed and explored as (i) dispersible capture agent, (ii) electrocatalyst, and (iii) nanoenzyme in miRNA detection are discussed.
PDF
