Proper Labeling Research Articles

An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation.

BackgroundIn the analysis of electronic health records, proper labeling of outcomes is mandatory. To obtain proper information from radiologic reports, several studies were conducted to classify radiologic reports using deep learning. However, the classification of pneumonia in bilingual radiologic reports has not been conducted previously.ObjectiveThe aim of this research was to classify radiologic reports into pneumonia or no pneumonia using a deep learning method.MethodsA data set of radiology reports for chest computed tomography and chest x-rays of surgical patients from January 2008 to January 2018 in the Asan Medical Center in Korea was retrospectively analyzed. The classification performance of our long short-term memory (LSTM)–Attention model was compared with various deep learning and machine learning methods. The area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, sensitivity, specificity, accuracy, and F1 score for the models were compared.ResultsA total of 5450 radiologic reports were included that contained at least one pneumonia-related word. In the test set (n=1090), our proposed model showed 91.01% (992/1090) accuracy (AUROCs for negative, positive, and obscure were 0.98, 0.97, and 0.90, respectively). The top 3 performances of the models were based on FastText or LSTM. The convolutional neural network–based model showed a lower accuracy 73.03% (796/1090) than the other 2 algorithms. The classification of negative results had an F1 score of 0.96, whereas the classification of positive and uncertain results showed a lower performance (positive F1 score 0.83; uncertain F1 score 0.62). In the extra-validation set, our model showed 80.0% (642/803) accuracy (AUROCs for negative, positive, and obscure were 0.92, 0.96, and 0.84, respectively).ConclusionsOur method showed excellent performance in classifying pneumonia in bilingual radiologic reports. The method could enrich the research on pneumonia by obtaining exact outcomes from electronic health data.

Current practices in the management of corrosive ingestion in children: A questionnaire-based survey and recommendations.

Corrosive ingestion causes significant morbidity in children with no standard guidelines regarding management. This survey aimed to understand practices adopted by gastroenterologists, identify lacunae in evaluation and management and suggest a practical algorithm. Indian gastroenterologists participated in an online survey (65 questions) on managing corrosive ingestion. When ≥ 50% of respondents agreed on a management option, it was considered as 'agreement'. Ninety-eight gastroenterologists (72 pediatric) who had managed a total of ~ 2600 corrosive ingestions in the last 5 years responded. The commonest age group affected was 2-5 years (61%). Majority of ingestion was accidental (89%) with 80% due to improper corrosive storage. Ingestion ofalkali and acid was equally common (alkali 41%, acid 39%, unknown 20%). History of inducing-vomiting after ingestion by community physicians was present in 57%.There was an agreement on 77% of questions. The respondents agreed on endoscopy (70%) and chest X-ray (67%) in all, irrespective of symptoms. Endoscopy was considered safe on days 1-5 after ingestion (91%) and relatively contraindicated thereafter. The consensus was to use acid suppression, always (59%); steroids, never (68%) and antibiotics, if indicated (59%). Feeding was based on endoscopic findings: oral in mild injuries and nasogastric (NG) in others. Eighty percent placed a NG tube under endoscopic guidance. Stricture dilatation was considered safe after 4 weeks of ingestion. Agreement on duration of acid suppression and stricture management (dilatation protocol and refractory strictures) was lacking. Corrosive ingestion mostly affects 2-5-year olds and is accidental in majority. It can be potentially prevented by proper storage and labelling of corrosives. An algorithm for management is proposed.

Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning.

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

Inclusive Lucky Labeling of Graphs

we define a new notion called inclusive lucky labeling (ILL) and study proper inclusive lucky labeling (PILL) for simple undirected graphs. We also define inclusive lucky number and proper inclusive lucky number for some simple graphs.

Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.

An investigation into country of origin labeling, species authentication and short weighting of commercially sold frozen fish fillets

Proper labeling of seafood is important to prevent economic deception and protect public health. The goal of this research was to investigate prepackaged frozen fish for Country of Origin Labeling (COOL) compliance, species labeling, net weights/short weighting, and percent glaze. A total of 111 frozen prepackaged fish fillets were purchased from grocery stores in Southern California (USA). Samples were designated as COOL compliant if they displayed both procurement method and country of origin in accordance with COOL requirements. Species labeling was examined by comparing the species identified with DNA barcoding to the acceptable market names provided in the FDA Seafood List. Net weights and percent glaze were determined by recording the weight of each product before and after deglazing. Of the 111 samples, only 1 was noncompliant with COOL and 10 samples (9%) were short-weighted. The average percent glaze was 5%, with seven samples having >10% glaze. Most fish (95.5%) were correctly labeled with regards to species. Species substitution was discovered in two samples and three samples had unacceptable market names. The results of this study indicate high COOL compliance and minimal species mislabeling in prepackaged frozen fish fillets. However, there is a need for increased focus on short weighting and/or overglazing of frozen fish products.

FDA Guidance on Medical Devices Containing Nitinol

Abstract New FDA guidance for Nitinol used in medical devices includes clarification on the determination of safety factors, the requirements for corrosion testing, and proper labeling of potentially harmful elements.

Lucky and Proper Lucky Labeling of Quadrilateral Snake Graphs

The labeling is said to be lucky labeling of the graph if the vertices of the graph are labeled by natural number with satisfying the condition that sum of labels over the adjacent of the vertices in the graph are not equal and if vertices are isolated vertex then the sum of the vertex is zero. The least natural number which labelled the graph is the lucky number. The Lucky Number of graph G is denoted by η(G). The labeling defined as proper labeling if the vertices of the graph are labeled by natural number with fulfilling the condition that label of adjacent vertices is not the same. The labeling is defined as proper lucky labeling if labeling is proper and also lucky. The proper lucky number of graph G is denoted by η p (G). Here we obtain a lucky number and proper lucky number for family of quadrilateral snake graph such as quadrilateral snake graph, double quadrilateral snake graph, alternate quadrilateral snake graph and double alternate quadrilateral snake graph.

Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets.

Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model's overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.

Understanding Antimicrobial Use Contexts in the Poultry Sector: Challenges for Small-Scale Layer Farms in Kenya.

The poultry sector contributes significantly to Kenya’s food and economic security. This contribution is expected to rise dramatically with a growing population, urbanization, and preferences for animal-source foods. Antimicrobial resistance is putting the poultry sector in Kenya—and worldwide—at risk of production losses due to the failure of medicines for animal (and human) health. The emergence and spread of antimicrobial resistance has been linked to overuse and misuse of antimicrobials in poultry and other sectors. Previous studies have documented poultry farmer antimicrobial use but without systematic consideration of the contexts (i.e., drivers) as important targets for behavior change, particularly in low- and middle-income countries. To improve understanding of antimicrobial use patterns in poultry systems, we conducted a mixed-methods knowledge, attitudes, and practices study of 76 layer farms in Kiambu County; Kenya. We found that commonly used antibiotics were often labeled for prophylactic, growth promotion, and egg production improvement purposes. Antimicrobial use was also motivated by the presence of diseases/disease symptoms, most of which could instead be managed through infection prevention measures. The results suggest that improving vaccination and biosecurity practices on farms and engaging with drug-makers to ensure proper labeling and marketing of antimicrobial drugs may represent important areas of opportunity for social behavior change communication and/or behavioral science interventions (i.e., nudges) to reduce disease burdens and promote prudent antimicrobial use. We conclude our findings with suggestions for further research into the behavioral insights at play in these scenarios to fuel future intervention development.

Updating the European Union's regulation on classification, labelling and packaging of substances and mixtures (CLP): A key opportunity for consumers, workers and stakeholders with interests in the legislation and toxicology of hazardous chemicals.

Recent advancements in toxicology and the European Union's Green Deal, with its Chemicals Strategy for Sustainability, have paved the way for major changes in EU legislation on the control of environmental chemicals for a cleaner and safer environment. Another substantial legislative advancement underway is the update of the "Regulation on Classification, Labelling and Packaging of Substances and Mixtures (CLP)," an ambitious piece of EU legislation with exceptional scientific toxicological background in identifying a hazard, aiming at better protecting its citizens and the environment from the risk of chemical substances and products, the occupational settings included. Update of CLP legislation additionally aims at facilitating the free exchange of chemicals in the European Internal Market, provided that proper labelling and packaging processes are implemented. Participation in the ongoing online public consultation on these issues, ending on November 15, 2021, is of key relevance to ensure a transparent and effective definition of such an important piece of legislation, fully compliant with current EU priorities in terms of human and environmental protection and animal welfare.

Microbial Community and Physicochemical Characterization of Kombuchas Produced and Marketed in Brazil

Kombucha has recently become popular in the Brazilian beverage market as a healthy alternative to soft drinks. However, little is known about the microbial composition and physicochemical characteristics of products available on the market. To investigate the microbial profile of kombuchas, high-throughput sequencing of the 16S rRNA and ITS genes, in samples belonging to six brands was utilized. In addition, the drinks were characterized based on the physicochemical parameters of pH, total acidity and alcohol content. Through the metagenetic analysis, the most abundant prokaryotic species identified were Liquorilactobacillus nagelii, Oenococcus oeni, Komagataeibacter rhaeticus, Liquorilactobacillus ghanensis, Gluconobacter oxydans, Komagataeibacter saccharivorans, Acetobacter peroxydans and Pantoea stewartii, while the mainly eukaryotic species were Dekkera bruxellensis, Dekkera anomala, Saccharomyces cerevisiae and Lanchancea fermentati. Interestingly, we identified six different oligotypes of D. bruxellensis, showing a wide diversity of strains belonging to this species. The results obtained for the physicochemical analyses, within the shelf life of the products, presented a range between 2.88 ± 0.06 and 3.43 ± 0.04 of pH, values between 1.80 ± 0.59 and 4.86 ± 0.72 for the total titratable acidity and 1.03 ± 0.24 to 2.54 ± 0.39 referring to alcohol content, demonstrating significant differences between brand. In addition, all samples had alcohol content above 0.5%, resulting in the classification of alcoholic beverages, which need proper labelling. The data generated in this work helped to understand the composition of the kombuchas available in the Brazilian market, as well as in the development of the identity and quality standard of the drink.

Prevalence of Extended Spectrum Beta-lactamase and Carbapenemase Producers in Gram Negative Bacteria causing Blood Stream Infection in Intensive Care Unit Patients

Introduction: Blood Stream Infections (BSI) in Intensive Care Unit (ICU) patients at initial stages are acute infections which might even become life threatening. In developing countries, increasing antimicrobial resistance and emergence of Extended Spectrum Beta-Lactamases (ESBL) and carbapenemase has also added an extra burden on physicians. Aim: To study the prevalence of emergence of ESBL and carbapenemase producing Gram Negative Bacteria (GNB) causing BSI in ICU patients. Materials and Methods: The present cross-sectional study was conducted on 1537 blood samples which were received in duration of two years from 2018 to 2020 in the Department of Microbiology, Maharishi Markandeshwar Institute of Medical Sciences and Research (MMIMSR), Mullana, Haryana, India from various ICUs. A 5-7 mL of blood was aseptically added to BACTEC bottles and bottles after proper labeling were inserted into the machine and incubated upto five days. 0.1 mL of broth from positively flagged bottles was cultured on Blood and MacConkey Agar. These plates were incubated at 37oC for 24 hours and processed as per standard microbiological procedures. Data was entered locally and calculated on the Microsoft Excel database. Results: Among 1537 samples, 263 (17.11%) samples were flagged positive by BACTEC system. On culture out of 263 samples, 51 (19.40%) were Gram Positive Cocci (GPC), 21 (07.98%) were Candida spp. and 191 (72.62%) were GNB. Among 191 Gram negative isolates, Escherichia coli 64 (33.51%) was the predominant organism followed by Klebsiella spp. 60 (31.41%). For all gram negative isolates, meropenem was the most sensitive drug followed by imipenem. Tigecycline (81.25%) was the second most effective drug against Acinetobacter baumannii. ESBL detection was done by Combine Disc Test on 124 samples (Escherichia coli and Klebsiella spp.) which showed Klebsiella spp. 25 (20.16%) as the highest ESBL producing organism The rate of carbapenemase producer was 20 (10.45%) among all the gram negative isolates. Conclusion: For BSI in ICU patients, culture and sensitivity along with screening for prevalence of ESBL and carbapenemase producers should be done prior to starting antibiotics.

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

Human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently. Features and information extracted from a person are known as soft biometric, they have been used to improve the recognition performance and enhance the search engine for face images, which can be further applied in various fields such as law enforcement, surveillance videos, advertisement, and social media profiling. By observing relevant studies in the field, we noted a lack of mention of the Arab world and an absence of Arab dataset as well. Therefore, our aim in this paper is to create an Arab dataset with proper labeling of Arab sub-ethnic groups, then classify these labels using deep learning approaches. Arab image dataset that was created consists of three labels: Gulf Cooperation Council countries (GCC), the Levant, and Egyptian. Two types of learning were used to solve the problem. The first type is supervised deep learning (classification); a Convolutional Neural Network (CNN) pre-trained model has been used as CNN models achieved state of art results in computer vision classification problems. The second type is unsupervised deep learning (deep clustering). The aim of using unsupervised learning is to explore the ability of such models in classifying ethnicities. To our knowledge, this is the first time deep clustering is used for ethnicity classification problems. For this, three methods were chosen. The best result of training a pre-trained CNN on the full Arab dataset then evaluating on a different dataset was 56.97%, and 52.12% when Arab dataset labels were balanced. The methods of deep clustering were applied on different datasets, showed an ACC from 32% to 59%, and NMI and ARI result from zero to 0.2714 and 0.2543 respectively.

Factors that Influence Acceptance of GM Labels on Halal Food Among Muslim Consumers

Genetically modified (GM) foods are produced from the development of modern biotechnology and gene combinations of the same or different species. GM foods have become an important issue among Muslim consumers in consuming halalan toyyiban food produced from GM foods. This in line with the decision of Muslim consumers in the selection of halal food that is closely related to faith, religion, and culture in the Islamic society. Labeling on food packaging is an important indicator of every food product in the market by Muslim consumers to determine the halalness of a food product. Therefore, the objective of this study is to determine factors that influence acceptance of GM labels on halal food among Muslim consumers. By using purposive sampling, 320 Muslim consumers were selected located in Klang Valley, Malaysia. An interviewer-administered questionnaire with the target consumers was carried out to obtain consumers’ responses. Several analyses were employed to analyze the data namely descriptive analysis, Chi-square analysis, Pearson correlation analysis, and factor analysis. The results of this study revealed that the majority of Muslim consumers were not aware of GM labels on halal food. There were significant associations between age, education level, and monthly income and Muslim consumers’ acceptance of GM labels on halal food. The relationship between awareness and acceptance showed a weak positive relationship. This study also revealed labeling characteristics, religious belief, and health concern are the three factors that influenced Muslim consumers’ acceptance of GM labeling on halal food. This study emphasizes that a continuous encouragement to food manufacturers in providing and displaying proper labeling on food products would assist Muslim consumers to enhance their awareness and acceptance of GM labels.

Sugar Spectrum, Hydroxymethylfurfural and Diastase Activity in Honey: A Validated Approach as Indicator of Possible Adulteration

Honey is used for centuries both as nutrient and in medicine, being an exhaustless source of benefic factors for health and also for different biotechnological processes. This is a complex matrix which possesses antioxidant and antimicrobial properties due to its peroxidic activity and bioactive compounds. A fully validated approach for the determination of sugars, hydroxymethylfurfural content and diastase activity in honey samples were presented in accordance with the official methods from the International Honey Commission. Spectrophotometric method was used for diastazic activity determination and chromatographic determinations for HMF content (photodiode array detection) and sugars (refractive index detection). The limits of detection and quantification were calculated. Accuracy, precision and uncertainty were determined in the spectrophotometric and chromatographic techniques using the certified reference material. Diastase activity, sucrose content and hydroxymethylfurfural (HMF) content were easily differentiated and these parameters were used for indication of a possible adulteration of the honey. The present study confirms that, in order to have an authentic honey sample, diastazic activity must be above 8 DN (diastazic number) and HMF content less than 40 mg/kg. Sugar spectrum is different for every honey type. Consequently, physico-chemical analysis should be combined for the determination of authentic honey and proper labeling.

Increasing Medication Access by Promoting Appropriate Use of Multi-dose Vials.

Medication shortage is a problem that affects patients, providers, and institutions of all sizes and scope across the United States. The objective of this quality improvement project was to promote the appropriate use of multi-dose vials (MDVs) by anesthesia providers at an independent plastic surgery office. Multi-dose vials can be used to decrease waste and potentially cost, thus increasing access to necessary medications for the patients at this practice. A focus group was used to obtain an understanding of barriers to the use of MDVs at this practice. A focused E-learning module on safe use based on established guidelines was then created, and a simplified flow sheet was implemented and placed in medication preparation areas as a cognitive aid. The education and flow sheet focused on identification and preparation of the medication area, proper identification of MDVs versus single-use vials, hand hygiene, proper beyond-use labeling, septum cleaning, use of a new sterile syringe and needle, and administration time frames. Provider feedback included high levels of satisfaction with the E-learning module. Our comparison of the use of ketamine from MDVs during the pre- and postimplementation phases showed a 14% increase in the number of doses used per vial. This finding suggests that were similar practices implemented at a larger site with MDVs of medications other than ketamine, resources could be impacted to manage shortages and increase access to medications.

Regeneration of hyaline cartilage in osteochondral lesion model using L-lysine magnetic nanoparticles labeled mesenchymal stem cells and their in vivo imaging.

Treatment of osteochondral defects continues to pose a major challenge for patients and orthopedic surgeons due to the limited healing potential of articular cartilage. Mesenchymal stem cells (MSCs) possess therapeutic potential for the treatment of osteochondral pain and pathology. However, it is necessary to use proper labeling and imaging agent of stem cells that can decipher its role posttransplantation. A major limitation of routinely used contrast agents is signal dilution over a period of time which limits its use for further studies. At the same time, regeneration of fibrocartilage over native hyaline cartilage also limits the use of conventional therapies. The present study evaluates the efficacy of bone marrow-derived mesenchymal stem cells (BMSCs) for the treatment of osteochondral defect in rats with the regeneration of hyaline cartilage in situ and in vivo monitoring of the stem cells using L-lysine functionalized magnetic iron oxide nanoparticles (lys-IONPs). L-lysine stabilizes the iron oxide nanoparticles, enhances the biocompatibility, and provides functionalities for efficient stem cell labeling. in vitro toxic effects of lys-IONPs on mitochondrial impairment, morphological alterations, and actin cytoskeleton reveal minimum damage to BM-MSCs. Histological data (H and E, Masson's trichrome and immunohistochemistry) describe the early initiation of healing and regeneration of hyaline-like cartilage over fibrocartilage in stem cell treated groups. MR scans demonstrate generation of hypointense signals in lys-IONPs-BMSCs with improved signal intensity and minimum loss over 28 days revealing its use as a long-term stem cell labeling and imaging agent.

Strategi Pengemasan dan Pelabelan Produk Jajanan Jadul di Kelurahan Siwalan Kecamatan Gayamsari Semarang

Businessman of old snack in Thematic Village, Siwalan Village, their marketing is still very limited and traditionally managed. Business actors also didn’t have proper product packaging and labeling. The aim of the Community Service Program is businessman of old snack able to implement packaging and labeling strategies in developing their products and spreading their market. This dedication activity is carried out with marketing management counseling methods, assistance in making packaging and labeling. The results of this activity are the businessman mastery of the packaging and labeling strategy and can apply simply online based marketing. Businessman of old snack are very enthusiastic to participate in this community service

Enabling sustainable critical materials for battery storage through efficient recycling and improved design: A perspective

Abstract