Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data

Background: A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.Results: Determined by obstetricians\u27 interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (\u3e96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (\u3e92%).Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management

Effect of Vitamin-D supplementation in adults presenting with chronic lower back pain

Background: Chronic pain in the lower back of adults is a common problem and mostly associated with Vitamin D deficiency. Along with standard treatment, vitamin D supplementation can help in early and better relief from back pain. Objective: To assess the effectiveness of vitamin D supplementation in patients with chronic lower back pain. Study Design & Methods: This Quasi-experimental trial was conducted at Department of Orthopaedics, Benazir Bhutto Hospital for 6 months. The patients aged between 15 to 55 years with chronic low back pain were included and pain score was noted by using a visual analogue scale (VAS). Patients were prescribed with oral vitamin D3 with a dose of 50,000 IU weekly for eight weeks (induction phase) and oral vitamin D3 with a dose of 50,000 IU once monthly for 6 months (maintenance phase). Outcome parameters included pain measured by VAS, functional disability by modified Oswestry disability questionnaire scores, and Vitamin-D3 levels at baseline,2, 3 and 6 months post-supplementation. Results: Mean age of patients was 44.21± 11.92 years.There were 337 (56.2%) male patients while 263 (43.8%) female patients. Baseline mean vitamin-D levels were 13.32 ± 6.10 ng/mL and increased to 37.18 ± 11.72 post supplementation (P < 0.0001). There was a significant decrease in the pain score after 2nd, 3rd& 6th months (61.7 ± 4.8, 45.2 ± 4.6 & 36.9 ± 7.9, respectively) than 81.2 ± 2.4 before supplementation (P < 0.001). The modified Oswestry disability score also showed significant improvement after 2nd, 3rd& 6thmonths (35.5 ± 11.4, 30.2 ± 9.4 & 25.8 ± 10.6, respectively) as compared to baseline 46.4 ± 13.2 (P < 0.001). About 418 (69.7%) patients attained normal levels after 6 months. Conclusion: Prescription of Vitamin D in addition to standard therapy for chronic lower back pain can be beneficial in getting relief from pain and improving the functional ability of the patient

Comparative Evaluation of Lamina Cribrosa Anatomical Parameters with Retinal Nerve Fiber Layer Thickness Defects In Primary Open-angle Glaucoma Cases And Controls

OBJECTIVES To assess the lamina cribrosa (LC) anterior lamina cribrosa depth (ALCD), lamina cribrosa thickness (LCT) and retinal nerve fiber layer thickness (RNFLT) in primary open-angle glaucoma (POAG) cases and age-matched controls and to compare these anatomical variables among POAG cases and age-matched controls. METHODOLOGY The case-control study was researched at Al-Ain Eye Institute, Karachi, in four month’s duration (November 2018 till February 2019). Expert eye specialist recruited 57 POAG cases and 46 age-matched healthy controls. Observation of intraocular pressure (IOP) and open angle was done using Goldmann tonometry and Slit-lamp biomicroscopy with stereoscopic ophthalmoscopy respectively. Visual field parameters of mean deviation (MD) and pattern standard deviation (PSD) measured using Humphrey Field Analyzer. Highly sensitive spectral domain ocular coherence tomography with enhanced depth imaging (EDI-OCT) was used to determine ALCD, LCT and RNFLT. RESULTS Statistically significant results were produced by RNFLT defects when it is compared in groups of mild with moderate cases of POAG (P-value 0.037). ALCD and LCT did display an association with RNFLT defects but did not produced statistically significant results. CONCLUSION Assessments of ALCD and LCT can provide important prognostic evidence about RNFLT and can assist in future planning of mild and moderate cases suffering from POAG

Frequency and Risk Factors of Depression among Medical Students: A Cross-Sectional Study in Karachi

OBJECTIVES The study aimed to determine the frequency of depression among medical students and to identify the different risk factors associated with depression. METHODOLOGY A cross-sectional study was conducted among medical students at a private medical college in Karachi. The study was initiated after approval was taken from the ethical committee. Consent was taken before the data collection after explaining the details of the study. Students were selected for this study as per inclusion criteria. They were provided with the PHQ-9 questionnaire in which they were inquired about the factors for depression. The total students with depression positive were presented by their frequencies with a 95% confidence interval. RESULTSThree hundred seventy medical students participated, and 207 (56%) tested positive for depression. Notably, depression was more prevalent among final-year students, with 80% affected. Additionally, the severity of depression gradually increased with advancing medical years, reaching the highest level in the final year, where 61 students (80%) reported significant depression. The most frequent causes of depression were living away from home and facing the challenges of a demanding curriculum. CONCLUSION The study findings revealed a higher likelihood of depression among medical students, particularly in their final year. This vulnerability was exacerbated by the stress associated with extensive coursework and peer pressure to achieve excellent exam grades

Trackable CEMB-Klean Cotton Transgenic Technology: Affordable Climate Neutral Agri-biotech Industrialization for Developing Countries

Background: Transgenic technology reflects the incorporation of novel useful traits in crop plants like cotton for economic benefits by overcoming the problems including insects’ pests and weeds in special. The present study is the success story of the continuous effort of CEMB team started back in the 1990s.Methods: This study includes characterization of a large number of Bacillus thuringiensis (Bt) strains taken from local soil and subjected to direct transformation of isolated BT genes into local cotton cultivars. Protocols for transformation into cotton plants were optimized and validated by the development of double gene codon optimized (Cry1Ac and Cry2A) transgenic cotton varieties.Results: The resulting GMOs in the form of CEMB-33, CA-12, CEMB-66 have been approved by Punjab Seed Council in 2013 and 2016 respectively. Double Bt and weedicide resistant cotton harboring CEMB-Modified and codon optimized cp4EPSPS (GTGene). These varieties can tolerate glyphosate spray @ 1900ml per acre without the appearance of necrotic spots/shedding and complete removal of all surrounding weeds in the cotton field is a significant advance to boost cotton production without spending much on insecticides and herbicides.Conclusion: In the current report, two unique sets of primers which amplify 1.1 Kb for CEMB-double Bt genes and 660 bp product for CEMB-Modified cp4EPSPS (GTGene) were tested. CEMB cotton variety CKC-01 is specially designed as low cost and easy to use by local farmer’s technology has the potential to revolutionize the cotton growing culture of the country.Keywords: Event detection; Bt Cotton; CEMB transgenic technology; GTGen

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe