Fast and accurate classification of echocardiograms using deep learning

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.Comment: 31 pages, 8 figure

Bimodal network architectures for automatic generation of image annotation from text

Medical image analysis practitioners have embraced big data methodologies. This has created a need for large annotated datasets. The source of big data is typically large image collections and clinical reports recorded for these images. In many cases, however, building algorithms aimed at segmentation and detection of disease requires a training dataset with markings of the areas of interest on the image that match with the described anomalies. This process of annotation is expensive and needs the involvement of clinicians. In this work we propose two separate deep neural network architectures for automatic marking of a region of interest (ROI) on the image best representing a finding location, given a textual report or a set of keywords. One architecture consists of LSTM and CNN components and is trained end to end with images, matching text, and markings of ROIs for those images. The output layer estimates the coordinates of the vertices of a polygonal region. The second architecture uses a network pre-trained on a large dataset of the same image types for learning feature representations of the findings of interest. We show that for a variety of findings from chest X-ray images, both proposed architectures learn to estimate the ROI, as validated by clinical annotations. There is a clear advantage obtained from the architecture with pre-trained imaging network. The centroids of the ROIs marked by this network were on average at a distance equivalent to 5.1% of the image width from the centroids of the ground truth ROIs.Comment: Accepted to MICCAI 2018, LNCS 1107

Implementasi Kompetensi Inti 1 Kurikulum 2013 Pada Mata Pelajaran PAI di Masa Pandemi Covid-19

Pembelajaran daring atau online dalam rangka untuk mencegah tersebarnya Covid 19 pada semua kehidupan, khususnya bagi kehidupan peserta didik. Keselamatan peserta didik menjadi prioritas utama dikeluarkannya kebijakan pemerintah yang berupa Surat Keputusan Bersama (SKB) empat Menteri, yakni Menteri Penddikan, Kebudayaan dan Riset dan Teknologi, Menteri Agama, Menteri Kesehatan, dan Menteri Dalam Negeri. Penelitian ini merupakan penelitian konseptual yang mencoba mengakaji dari beberapa literatur yang relevan dengan judul dan kemudian dianalisis dengan fenomena pembelajaran di masa pendemi covid 19. Penelitian yang relevan dengan penelitian ini sebenarnya sudah pernah dilakukan oleh peneliti sebelumnya, namun dilakukan secara umum. Dalam penelitian ini, peneliti mencoba fokus pada implementasi kompetensi inti 1 kurikulum 2013 di masa pandemi covid 19. Penelitian ini bertujuan: 1) bagaimana pembelajaran di masa Pandemi Covid-19; apa kompetensi inti 1 dalam kurikulum 2013; dan 3) implementasi kompetensi inti 1 pada mata pelajaran PAI di masa pandemi covid 19. Harapan penelitian ini: 1) menjadi bahan evaluasi bagi guru dalam melaksanakan proses pembelajaran daring atau online di masa pandemi covid 19; 2) menjadi bahan evaluasi dalam implementasi kompetensi inti 1 pada mata pelajaran PAI serta sebagai solusi problem pembelajaran daring atau online

Effect of water stress, chemical and organic fertilizers on yield and yield components of rosemary (Rosmarinus officinalis L.)

To improve crop productivity under water stress conditions, understanding the interaction between fertilizers and water stress is very important for the proper management of fertilizers consumption. The present study was aimed at investigating the impact of water stress, and chemical and organic fertilizers on yield and yield components of rosemary (Rosmarinus officinalis L.). The experiment was carried out in Ferdows, Iran in 2015 in the form of split plots based on a randomized complete block design. 50% and 80% of water requirements were allocated to the main plots. Five kinds of fertilizers including organic fertilizer before planting (F1), foliar application of Macromix Gold organic fertilizer (F2), NPK chemical fertilizer before planting (F3), and foliar application of NPK chemical fertilizer after planting (F4), and no fertilizer (control, F5) were allocated to subplots. Results indicate that in conditions of 80% water requirement, fertilizer treatments of F2 and F4, and in conditions of 50% water requirement, fertilizer treatment F4 had the highest percentage of essential oil (more than 2%). Concerning dry matter, in both conditions of 50% and 80% water requirement, all fertilizer treatments had more dry matter than the control. Results demonstrated that in terms of achieving the maximum percentage of essential oil in conditions of 80% of water requirement, only chemical fertilizer as a foliar application (F4) is recommended, but in the case of 50% of water requirement, foliar application of Macromix Gold organic fertilizer (F2) can be a suitable alternative to chemical fertilizer (F4). Considering the achievement of maximum dry matter in both 80% and 50% treatments, organic fertilizer before planting (F1) is an economically and environmentally appropriate treatment

Isolation and identification of inhibitory bacteria against pathogenic fungi from Isfahan using molecular method

زمینه و هدف: گونه های باسیلوس منبعی از متابولیت های ضد قارچی با توان مهار عفونت های قارچی هستند. هدف از این مطالعه، جداسازی و شناسایی باکتری مهارکننده ی رشد قارچ های پاتوژن از اصفهان با استفاده از روش مولکولی بود. روش بررسی: در این مطالعه توصیفی- مقطعی، تعداد 150 نمونه (خاک، هوا و سطوح) از شهر اصفهان تهیه و تأثیر مهاری باکتری های رشد یافته بر روی محیط کشت نوترینت آگار بر رشد قارچ های آسپرژیلوس نایجر، آسپرژیلوس فلاووس و موکور هیمالیس بررسی شد. بررسی کیفی مهار رشد قارچ با روش نشاکاری و جهت بررسی کمی مهار رشد قارچ ها تلقیح سوسپانسیون قارچی حاوی 104 اسپور بر میکرولیتر به صورت کشت خطی در فواصل 5/0، 1، 5/1، 2، 5/2 و 3 سانتی متری از مرکز (محل تلقیح سوسپانسیون 5/0 مک فارلند باکتری ها) انجام شد. نمونه ها در دمای 30 درجه سانتی گراد به مدت 96 ساعت نگهداری و شناسایی باکتری مهاری با تست های بیوشیمیایی و روش مولکولی انجام گرفت. یافته ها: تأثیر مهاری باکتری ها بر رشد قارچ های آسپرژیلوس نایجر، آسپرژیلوس فلاووس و موکور هیمالیس در فواصل 5/0 تا 3 سانتی متر مشاهده شد. بر اساس نتایج تست های بیوشیمیایی و روش کلنی- PCR، باکتری با بیشترین اثر مهاری نسبت به قارچ های مذکور باسیلوس آتروفئوس سویه ی HNSQJYH170 شناسایی شد. نتیجه گیری: باسیلوس آتروفئوس سویه ی HNSQJYH170 بومی اصفهان قابل استفاده برای تولید آنتی بیوتیک و مصارف کنترل بیولوژیک است

An Appraisal of Sheikh abid Sindhi`s Tawali -al- Anwar Sharh Durr –ul- Mukhtar: A Jurisprudential Analysis

Sheikh Abid Sindhi (d. 1252 A.H) was one of the most distinguished Islamic scholars, jurists, and prolific authors of the Hanafī School of jurisprudence in the twelfth-century A.H from Sindh. He was widely regarded as one of the foremost experts on Hadīth and one of the omniscient men of the recent era of Hanafī Jurisprudence. His expertise in various branches of Islamic knowledge is unique. The most monumental and significant work of Sheikh Abid Sindhi is a commentary on Durr -ul- Mukhtar named: Tawali -al- Anwar Sharh -al- Durr -ul- Mukhtar. Though the commentaries on Durr -ul- Mukhtar are many and varied, the most famous and widespread commentary is Radd -al- Muhtar by Muhammad Amin ibn Abidin Shami (d.1252 A.H). This book is very crucial among Islamic scholars, but Tawali -al- Anwar is a comprehensive, extensive, rich, and authoritative commentary from every aspect of research. It was studied from the Qur‘ānic perspective, referenced from the verses of the Holy Qur‘ān, Qurānic exegesis (Tafsīr), Hadīths, Science of Hadīth, and jurisprudential approaches of Hanafī scholars and intellectual evidence. Undoubtedly, it deserves to be acknowledged as the finest and comprehensive and informative commentary on Durr-ul-Mukhtar. This study focuses on the author`s biography, methodology, and its importance in the Hanafī School of jurisprudence. Keywords: Abid Sindhi Tawali-al-Anwar Sharh-al-Durr-ul-Mukhtar, Hanafī School of Jurisprudence, Qurānic exegesis, Tafsīr, Hadīths, Science of Hadīth

Avoiding Water Bankruptcy in the Drought-Troubled Southwest: What the US and Iran Can Learn from Each Other

The 2021 water year ends on Sept. 30, and it was another hot, dry year in the western U.S., with almost the entire region in drought. Reservoirs vital for farms, communities and hydropower have fallen to dangerous lows. The biggest blow came in August, when the U.S. government issued its first ever water shortage declaration for the Colorado River, triggering water use restrictions. In response, farmers and cities across the Southwest are now finding new, often unsustainable ways to meet their future water needs. Las Vegas opened a lower-elevation tunnel to Lake Mead, a Colorado River reservoir where water levels reached unprecedented lows at 35% of capacity. Farmers are ratcheting up groundwater pumping. Officials in Arizona, which will lose nearly one-fifth of its river water allotment under the new restrictions, even floated the idea of piping water hundreds of miles from the Mississippi River