Enhanced Arabic disaster data classification using domain adaptation.

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey.

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Designing Multimodal Interactive Dashboard of Disaster Management Systems.

Disasters and crises are inevitable in this world. In the aftermath of a disaster, a society's overall growth, resources, and economy are greatly affected as they cause damages from minor to huge proportions. Around the world, countries are interested in improving their emergency decision-making. The institutions are paying attention to collecting different types of data related to crisis information from various resources, including social media, to improve their emergency response. Previous efforts have focused on collecting, extracting, and classifying crisis data from text, audio, video, or files; however, the development of user-friendly multimodal disaster data dashboards to support human-to-system interactions during an emergency response has received little attention. Our paper seeks to fill this gap by proposing usable designs of interactive dashboards to present multimodal disaster information. For this purpose, we first investigated social media data and metadata for the required elicitation and analysis purposes. These requirements are then used to develop interactive multimodal dashboards to present complex disaster information in a usable manner. To validate our multimodal dashboard designs, we have conducted a heuristic evaluation. Experts have evaluated the interactive disaster dashboards using a customized set of heuristics. The overall assessment showed positive feedback from the evaluators. The proposed interactive multimodal dashboards complement the existing techniques of collecting textual, image, audio, and video emergency information and their classifications for usable presentation. The contribution will help the emergency response personnel in terms of useful information and observations for prompt responses to avoid significant damage.

Anatomical characterization of 18 commercially important varieties of Phoenix dactylifera L. by using microscopy.

Date palm (Phoenix dactylifera L.) is an important traditional crop having socioeconomic values. To best of literature review and our knowledge, there is a gap of taxonomic study on the role of morpho-anatomical features of date palm for correct identification of its varieties. With the advancement in the field of microscopy, anatomical features are considered very effective taxonomical markers. The present study was aimed to analyze the taxonomic variations using leaf epidermal anatomy and to highlight the importance of leaf epidermis to resolve identification ambiguities among the different varieties of date palms. The study includes analysis of leaf epidermal anatomy of 18 different commercial varieties of date palm collected from different areas of Pakistan. A diverse range of shape and size of epidermal cells, subsidiary cells, guard cells, stomata, stomatal pore was observed on abaxial as well as adaxial among studied date palm varieties via LM and found to be very helpful tool in discrimination of varieties. The shape of epidermal cells on both abaxial and adaxial surfaces varies including elongated, irregular, polygonal, curved, and spherical or sometime mixed of these. The values of stomatal indices on both surfaces showed wide variations.

Hypermetabolic Hurthle Cell Adenoma on 18F-FDG PET/CT.

Thyroid incidentalomas are frequently reported on 18F-FDG PET/CT scan. High risk of malignancy is thought to be associated with increased metabolic activity and high standardized uptake value. Likewise, thyroid nodules with focal FDG avidity have a higher potential to be malignant. However, some benign nodules such as follicular and Hurthle cell adenomas can also present with focal hypermetabolic activity. We report a case of a 59-year-old lady diagnosed with gastric carcinoma, who had a hypermetabolic thyroid nodule on FDG PET/CT scan. Despite the complex texture of the nodule and intense focal avidity, the histopathology was consistent with Hurtle cell adenoma.

Fluorescence in Situ Hybridization (FISH) for Differential Diagnosis of Soft Tissue Sarcomas

Introduction:Soft tissue sarcomas are rare tumors comprising 1 percent of solid malignancies. The latest edition of WHO soft tissue pathology lists 94 benign and malignant soft tissue tumors. Many of these show a large degree of morphological overlap. Immunohistochemistry has been shown to be reliable in many cases for differential diagnosis of lesions, although cytogenetic tests are considered the gold standard for many entities.Fluorescence in-situ hybridization (FISH) is a cytogenetic technique that uses fluorescent probes that bind to only those parts of the chromosome which have a high degree of sequence complementarity. Many soft tissue tumors show recurrent genetic mutations that are now being used as diagnostic markers. Knowledge of the molecular identity allows prediction of behavior, prognosis and treatment response. Objective:The aim of this study was to identify genetic mutations in soft tissue sarcomas using FISH testing and to assess correlations with histological diagnosis. Material and methods:A total of 25 cases of different soft tissue sarcomas diagnosed on histology with the help of immunohistochemical staining and for which FISH studies were requested were included in this study. Three pathologists with a special interest in soft tissue sarcomas reviewed the cases. FISH tests for EWS, the X:18 translocation, FOXO1 and MDM2 were respectively applied for 8 cases of Ewing sarcoma, 8 cases of synovial sarcoma, 2 cases of rhabdomyosarcoma and 7 cases of dedifferentiated liposarcoma and atypical lipomatous tumors/well differentiated liposarcomas. Results:EWS gene fusion was detected in 7 out of 8 cases of Ewing sarcoma and the X:18 translocation was positive in 3 of the 8 cases of synovial sarcoma. FOXO1 was not detected in either of the two rhabdomyosarcomas. MDM2 by FISH was detected in only one out of 5 cases of atypical lipomatous tumors and 1 out of 2 dedifferentiated liposarcomas. Conclusion: FISH is a useful adjunct in the diagnostic assessment of different types of soft tissue sarcomas. It is easy to set up, is relatively inexpensive and has the ability to diagnose sarcomas with great accuracy, especially in cases which can not be accurately classified even after thorough histological and immunohistochemical evaluation. It may play a very important role in the accurate diagnosis and correct management of patients.