Restricting family life - an examination of citizens' views on state interventions and parental freedom in eight European countries.

This paper examines the public views - a total of 10,348 persons - on restrictions of personal autonomy of others to protect the interest of children. We use representative country samples of the adult populations of Austria, England, Estonia, Finland, Germany, Ireland, Norway, and Spain, and ask them to consider an experimental vignette with three different parental conditions: substance abuse, mental health problems, and learning difficulties. The findings display that most people would restrict parental freedom to protect the child, and a stricter restriction when the parent struggles with substance abuse compared to mental health compared to learning difficulties. There are some country differences, and when examining the role of institutional context of child protective system, a correlation is detected with significant differences between population views in a right-oriented system versus a well-being system and maltreatment system. In light of the ongoing European debates about child protection and how controversial and contested this area of the welfare state seem to be, it is interesting to learn (also) from this study that people, across countries, individual differences, child protection systems, overall are supportive of state intervention and support in a situation with a child at potential risk.

Advancements in Perovskite-Based Cathode Materials for Solid Oxide Fuel Cells: A Comprehensive Review.

The high-temperature solid oxide fuel cells (SOFCs) are the most efficient and green conversion technology for electricity generation from hydrogen-based fuel as compared to conventional thermal power plants. Many efforts have been made to reduce the high operating temperature (>800 °C) to intermediate/low operating temperature (400 °C

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data. Thanks to the availability of large datasets, the development of AI-based methods for lesion segmentation has immensely accelerated in the last decade. One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. This systematic review offers an appraisal of the impact of the ATLAS dataset in promoting the development of AI-based segmentation of stroke lesions. An examination of all published studies, that used the ATLAS dataset to both train and test their methods, highlighted an overall moderate performance (median Dice index = 59.40%) and a huge variability across studies in terms of data preprocessing, data augmentation, AI architecture, and the mode of operation (two-dimensional versus three-dimensional methods). Perhaps most importantly, almost all AI tools were borrowed from existing AI architectures in computer vision, as 90% of all selected studies relied on conventional convolutional neural network-based architectures. Overall, current research has not led to the development of robust AI architectures than can handle spatially heterogenous lesion patterns. This review also highlights the difficulty of gauging the performance of AI tools in the presence of uncertainties in the definition of the ground truth.

Knowledge distillation driven instance segmentation for grading prostate cancer.

Prostate cancer (PCa) is one of the deadliest cancers in men, and identifying cancerous tissue patterns at an early stage can assist clinicians in timely treating the PCa spread. Many researchers have developed deep learning systems for mass-screening PCa. These systems, however, are commonly trained with well-annotated datasets in order to produce accurate results. Obtaining such data for training is often time and resource-demanding in clinical settings and can result in compromised screening performance. To address these limitations, we present a novel knowledge distillation-based instance segmentation scheme that allows conventional semantic segmentation models to perform instance-aware segmentation to extract stroma, benign, and the cancerous prostate tissues from the whole slide images (WSI) with incremental few-shot training. The extracted tissues are then used to compute majority and minority Gleason scores, which, afterward, are used in grading the PCa as per the clinical standards. The proposed scheme has been thoroughly tested on two datasets, containing around 10,516 and 11,000 WSI scans, respectively. Across both datasets, the proposed scheme outperforms state-of-the-art methods by 2.01% and 4.45%, respectively, in terms of the mean IoU score for identifying prostate tissues, and 10.73% and 11.42% in terms of F1 score for grading PCa according to the clinical standards. Furthermore, the applicability of the proposed scheme is tested under a blind experiment with a panel of expert pathologists, where it achieved a statistically significant Pearson correlation of 0.9192 and 0.8984 with the clinicians' grading.

Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes.

Metasurfaces, a special class of metamaterials, have recently become a rapidly growing field, particularly for thin polarization converters. They can be fabricated using a simple fabrication process due to their smaller planar profile, both in the microwave and optical regimes. In this paper, the recent progress in MSs for linear polarization (LP) to circular polarization (CP) conversion in transmission mode is reviewed. Starting from history, modeling and the theory of MSs, uncontrollable single and multiple bands and LP-to-CP conversions, are discussed and analyzed. Moreover, detailed reconfigurable MS-based LP-to-CP converters are presented. Further, key findings on the state-of-the-arts are discussed and tabulated to give readers a quick overview. Finally, a conclusion is drawn by providing opinions on future developments in this growing research field.

CNN-Based Smoker Classification and Detection in Smart City Application.

To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time.

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability. METHOD: The proposed RFS-Net architecture integrates the atrous spatial pyramid pooling (ASPP), residual, and inception modules in the encoder path to learn better features and conserve more global information for precise segmentation and characterization of MRF lesions. The RFS-Net model is trained and validated using OCT scans from multiple vendors (Topcon, Cirrus, Spectralis), collected from three publicly available datasets. The first dataset consisted of OCT volumes obtained from 112 subjects (a total of 11,334 B-scans) is used for both training and evaluation purposes. Moreover, the remaining two datasets are only used for evaluation purposes to check the trained RFS-Net's generalizability on unseen OCT scans. The two evaluation datasets contain a total of 1572 OCT B-scans from 1255 subjects. The performance of the proposed RFS-Net model is assessed through various evaluation metrics. RESULTS: The proposed RFS-Net model achieved the mean F1 scores of 0.762, 0.796, and 0.805 for segmenting IRF, SRF, and PED. Moreover, with the automated segmentation of the three retinal manifestations, the RFS-Net brings a considerable gain in efficiency compared to the tedious and demanding manual segmentation procedure of the MRF. CONCLUSIONS: Our proposed RFS-Net is a potential diagnostic tool for the automatic segmentation of MRF (IRF, SRF, and PED) lesions. It is expected to strengthen the inter-observer agreement, and standardization of dosimetry is envisaged as a result.

The predictive value of absolute lymphocyte counts on tumor progression and pseudoprogression in patients with glioblastoma.

BACKGROUND: Differentiating true glioblastoma multiforme (GBM) from pseudoprogression (PsP) remains a challenge with current standard magnetic resonance imaging (MRI). The objective of this study was to explore whether patients' absolute lymphocyte count (ALC) levels can be utilized to predict true tumor progression and PsP. METHODS: Patients were considered eligible for the study if they had 1) GBM diagnosis, 2) a series of blood cell counts and clinical follow-ups, and 3) tumor progression documented by both MRI and pathology. Data analysis results include descriptive statistics, median (IQR) for continuous variables and count (%) for categorical variables, p values from Wilcoxon rank sum test or Fisher's exact test for comparison, respectively, and Kaplan-Meier analysis for overall survival (OS). OS was defined as the time from patients' second surgery to their time of death or last follow up if patients were still alive. RESULTS: 78 patients were included in this study. The median age was 56 years. Median ALC dropped 34.5% from baseline 1400 cells/mm3 to 917 cells/mm3 after completion of radiation therapy (RT) and temozolomide (TMZ). All study patients had undergone surgical biopsy upon MRI-documented progression. 37 had true tumor progression (47.44%) and 41 had pseudoprogression (52.56%). ALC before RT/TMZ, post RT/TMZ and at the time of MRI-documented progression did not show significant difference between patients with true progression and PsP. Although not statistically significant, this study found that patients with true progression had worse OS compared to those with PsP (Hazard Ratio [HR] 1.44, 95% CI 0.86-2.43, P = 0.178). This study also found that patients with high ALC (dichotomized by median) post-radiation had longer OS. CONCLUSION: Our results indicate that ALC level in GBM patients before or after treatment does not have predictive value for true disease progression or pseudoprogression. Patients with true progression had worse OS compared to those who had pseudoprogression. A larger sample size that includes CD4 cell counts may be needed to evaluate the PsP predictive value of peripheral blood biomarkers.

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.

Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study.

This study aims to provide estimates, trends and projections of vision loss burden in Pakistan from 1990 to 2025. Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017) was used to observe the vision loss burden in terms of prevalence and Years Lived with Disability (YLDs). As of 2017, out of 207.7 million people in Pakistan, an estimated 1.12 million (95% Uncertainty Interval [UI] 1.07-1.19) were blind (Visual Acuity [VA] <3/60), 1.09 million [0.93-1.24] people had severe vision loss (3/60≤VA<6/60) and 6.79 million [6.00-7.74] people had moderate vision loss (6/60≤VA<6/18). Presbyopia was found to be the most common ocular condition that affected an estimated 12.64 million [11.94-13.41] people (crude prevalence 6.08% [5.75-6.45]; 61% female). In terms of age-standardized YLDs rate, Pakistan is ranked fourth among other South Asian countries and twenty-first among other 42 low-middle income countries (classified by World Bank), with 552.98 YLDs [392.98-752.95] per 100,000. Compared with 1990, all-age YLDs count of blindness and vision impairment increased by 55% in 2017, which is the tenth highest increase among major health loss causes (such as dietary iron deficiency, headache disorders, low back pain etc.) in Pakistan. Moreover, our statistics show an increase in vision loss burden by 2025 for which Pakistan needs to make more efforts to encounter the growing burden of eye diseases.

Neutrophil-to-lymphocyte ratio as a predictor of survival in patients with triple-negative breast cancer.

PURPOSE: Peripheral blood lymphopenia and elevated neutrophil-to-lymphocyte ratio (NLR) have been associated with poor outcomes in various malignancies. However, existing literature has largely focused on baseline parameters. The aim of this study is to assess the impact of radiation therapy (RT) and chemotherapy on absolute lymphocyte counts (ALC) and NLR in relation to survival outcomes in patients with triple-negative breast cancer (TNBC). METHODS: A retrospective analysis was performed on 126 patients with TNBC treated at Washington University between 2005 and 2010. Cox proportional hazard model with time-varying covariates was applied to estimate the effect of time-varying ALC and NLR separately on overall survival (OS) and disease-free survival (DFS). RESULTS: All patients received RT and 112 patients received either neoadjuvant chemotherapy or adjuvant chemotherapy, or both. Patients deceased had lower ALC and higher NLR compared to patients alive throughout the treatment course, even 1 year after treatment completion (ALC, 1 vs. 1.3, P = 0.03 and NLR, 3.9 vs. 2.6, P = 0.03). High ALC was associated with superior OS on both continuous and binary scales (cutoff of 1 K/ul) (HR 0.14; 95% CI 0.05-0.34; P < 0.001 and HR 0.28; 95% CI 0.13-0.61; P = 0.01, respectively). Additionally, high NLR was weakly associated with inferior OS on continuous scales (HR 1.1; 95% CI 1.06-1.15; P < 0.001). CONCLUSIONS: Post-treatment lymphopenia and NLR elevation can persist until 1 year after treatment completion. Both portend shorter survival for patients with TNBC. Our data support the use of ALC and NLR to identify high risk patients who may benefit from clinical trials rather than standard of care therapy.

Recent advances on polysaccharides, lipids and protein based edible films and coatings: A review.

Food is a vital product for the survival of human beings and with passage of time quality concerns of consumers are rising. Edible films and coatings are thin layers applied on food products to protect them and improve their quality. Films/coatings are prepared from naturally occurring renewable sources (polysaccharides, proteins, lipids and composites) which we can eat without disposing them. These films are environment friendly and contain antioxidants, anti-browning agents and colorants. Various methods (spraying, brushing, electro-spraying) are used to apply a coating on food material to protect them from microbial growth, prolonging their shelf life and improving other quality aspects like sensory attributes, appearance, originality and freshness of ingredients. In addition to edible films, some special additives like glycerol, sorbitol etc. is used to improve the efficiency of edible films and coatings. Chemistry and nature of these films and coatings vary in the vast range of hydrophilic and hydrophobic boundaries to cover the whole range of food products. In recent times, herbal coatings are widely used for the coating purposes e.g. Aloe Vera, citral and eugenol essential oils. However, some challenges presented are focusing the scientific attention for viable solution.

Outcomes of High Risk Patients with Febrile Neutropenia at a Tertiary Care Center

Fever during chemotherapy-induced neutropenia continues to be a major cause of morbidity and mortality in cancer patients. Mortality depends on the duration and degree of neutropenia, bacteremia, sepsis, performance status, comorbidities and other parameters. The highest mortality rates in cancer patients hospitalized with febrile neutropenia (FN) are observed in those with documented infection. The objectives of the study were to present available tools for risk assessment, to review pathogens causing infections in adult FN patients and to assess outcomes. Methods: This cross sectional study was conducted on adult culture positive FN patients admitted to the Hematology/Oncology service at the Aga Khan University Hospital, Karachi, Pakistan from 1st January 2009 to 31st December 2012. Highrisk criteria were defined as profound neutropenia, short latency from a previous chemotherapy cycle, sepsis or clinically documented infection at presentation, severe co-morbidity and a performance status greater than or equal to 3. All types of organisms in blood culture and the outcomes of the patients were recorded on Proforma. Results: A total of 156 patients with culture-positive febrile neutropenia were identified during the study period. The mean age was 47 years with a slight male predominance of 54%. One hundred and sixteen patients fulfilled the criteria for the high risk group. Fifty two percent had a single high risk factor and 40 % had two. All patients harbored either single or multiple bacterial organisms including gram positive, gram negative or both types. Some 34% of patients had gram positive bacteremia, 57 % had gram negative and 9 % were infected with both. Among 73 gram positive cultures 44 % were Staphylococcus species and among 123 gram negative cultures 43 % were E. coli. One hundred and fifteen patients recovered uneventfully and could be discharged. Thirty two patients in the high risk and 9 in the low risk groups deceased with an overall mortality of 26 %. The mean hospital stays of patients with solid tumors and hematological malignancies were 7.58 and 15.0 days, respectively. Mortality was higher in the latter group, and also in high risk patients with both gram positive and negative bacteremia. Conclusion: We emphasize the importance of risk stratification and continuous surveillance of the spectrum of locally prevalent pathogens and their susceptibility patterns for formulation of therapeutic regimens for febrile neutropenic patients.

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Automated segmentation of subretinal layers for the detection of macular edema.

Macular edema (ME) is considered as one of the major indications of proliferative diabetic retinopathy and it is commonly caused due to diabetes. ME causes retinal swelling due to the accumulation of protein deposits within subretinal layers. Optical coherence tomography (OCT) imaging provides an early detection of ME by showing the cross-sectional view of macular pathology. Many researchers have worked on automated identification of macular edema from fundus images, but this paper proposes a fully automated method for extracting and analyzing subretinal layers from OCT images using coherent tensors. These subretinal layers are then used to predict ME from candidate images using a support vector machine (SVM) classifier. A total of 71 OCT images of 64 patients are collected locally in which 15 persons have ME and 49 persons are healthy. Our proposed system has an overall accuracy of 97.78% in correctly classifying ME patients and healthy persons. We have also tested our proposed implementation on spectral domain OCT (SD-OCT) images of the Duke dataset consisting of 109 images from 10 patients and it correctly classified all healthy and ME images in the dataset.

Awareness of autism in primary school teachers.

Objective. To assess the knowledge and perception of primary school teachers regarding autism in private and public schools of Karachi, Pakistan. Methods. A cross-sectional survey was conducted on primary school teachers in different districts of Karachi. A sample size of 170 teachers was selected by purposive sampling. Primary data was collected using self-administered questionnaires. These questions assessed the teacher's knowledge and perception of Autism. Data was entered on SPSS version 20. Frequencies and percentages were taken out for categorical variables. Results. Of the total 170 teachers, 85 were from the Private and 85 from Public sector schools. 55% (n = 94) of the teachers knew about Autism through the media and only 9% (n = 15) had formal training through workshops on Autism. 62% (n = 105) of the teachers were of the opinion that Autism is treatable. Majority of the teachers (57%) said that proper training is required for teaching autistic children. Conclusion. The knowledge related to Autism in our existing sample has mostly come from the media. Although we cannot undermine the role of media, there is a need to give formal training to teachers regarding the differentiating features of Autism, which in turn will aid in early diagnosis of the disease.