A case of MINOCA in a patient with recent history of COVID-19 infection

Background: Myocardial infarction with nonobstructive coronary arteries (MINOCA) is a syndrome of myocardial ischemia resulting from microvascular dysfunction and with \u3c 50% stenosis of major epicardial vessels. The incidence of MINOCA is 6% among patients with acute myocardial infarction. We present a case of MINOCA in a patient with a recent history of COVID-19 infection. Case presentation: A 22-year-old man with recent history of Covid 19 infection presented with 3 days history of typical cardiac chest pain. He was not taking any medications or illicit drugs. EKG revealed sinus rhythm with ST elevations in leads II, V5, V6. Troponin I was elevated to 5.3ng/ml. He underwent coronary angiography which was reported as normal with no signs of obstructive coronary artery disease. Further workup including viral panel, ESR, CRP, HIV, hepatitis panel were negative. He was discharged on clopidogrel, metoprolol and rosuvastatin. His clinical course was significant for recurrence of similar symptoms 2 months later, with EKG revealing similar pattern as prior. Cardiac CT was negative for pericardial thickening or any other cardiac abnormalities. He was started on aspirin and colchicine for suspected post-Covid myopericarditis, resulting in resolution of his symptoms. Conclusion: Diagnosis of MINOCA should include recognizing underlying mechanism as it would help in the management. Common reversible etiologies of MINOCA are microvascular dysfunction, spasm and thrombophilia disorders. Interestingly, COVID-19 infection has been recognized as a thrombophilic state. While the management of overt coronary artery disease is well established, the benefits of reperfusion strategies and cardioprotective therapies in MINOCA require further investigation

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are currently applied to produce the best commercial LLMs (e.g., ChatGPT). To improve the accessibility of LLMs for research and development efforts, various instruction-tuned open-source LLMs have also been introduced recently, e.g., Alpaca, Vicuna, to name a few. However, existing open-source LLMs have only been instruction-tuned for English and a few popular languages, thus hindering their impacts and accessibility to many other languages in the world. Among a few very recent work to explore instruction tuning for LLMs in multiple languages, SFT has been used as the only approach to instruction-tune LLMs for multiple languages. This has left a significant gap for fine-tuned LLMs based on RLHF in diverse languages and raised important questions on how RLHF can boost the performance of multilingual instruction tuning. To overcome this issue, we present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research. We also present benchmark datasets to enable the evaluation of generative LLMs in multiple languages. Our experiments demonstrate the advantages of RLHF for multilingual instruction over SFT for different base models and datasets. Our framework and resources are released at https://github.com/nlp-uoregon/Okapi

Control of Vibrio parahaemolyticus (AHPND strain) and improvement of water quality using nanobubble technology

Nanobubble technology is used in wastewater treatment, but its disinfectant properties in aquaculture have not been clearly demonstrated. This study investigated the ability of nanobubbles to reduce Vibrio parahaemolyticus (AHPND strain) and to improve water quality. Two laboratory experiments were conducted over a one‐week period, that is (a) assessing the effects of air and oxygen nanobubbles for 60 minutes per day and (b) comparing effects of ozone nanobubble treatments for 2, 4 and 6 minutes per day. Experiments were done in triplicate 100 L tanks with 15‰ saline water, inoculated with an initial bacterial concentration of 106 CFU/ml. At the end of experiment 1, the bacterial concentration of the air and oxygen nanobubble groups was counted for 69% and 46% of the control group respectively. At the end of experiment 2, the bacterial concentration of the 2‐, 4‐ and 6‐minute ozone nanobubble groups were counted for 23%, 2.2% and 0% of the control group respectively. Oxygen and ozone nanobubbles significantly increased oxygen reduction potential and oxygen values. Results indicate that under effective dosages nanobubbles can be used in the production farms to control V.parahaemolyticus and increase oxygen levels

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.Comment: Ongoing Wor

Screening of endophytes from rubber trees (hevea brasiliensis) for biological control of Corticium salmonicolor

28 leaves and living-tissue samples of rubber tree (Hevea brasiliensis) were collected from Ho Chi Minh City, Binh Phuoc province and Binh Duong province (Viet Nam). We isolated and screened endophytes that have potential application as agents for biocontrol of Corticium salmonicolor, the agent of Pink Disease in rubber trees. As a result, 21 strains of endophytic bacteria and 14 strains of endophytic fungi were isolated. Antagonistic activity of the endophytes towards C. salmonicolor was checked by using a dual culture. Testing results showed that: T9, T15 and T16 strains have inhibited C. salmonicolor. T9 and T16 strains showed result that 100% of inhibiting C. salmonicolor at the concentration of 1:1. In the test of ability to kill C. salmonicolor, T9 and T16 strains showed that they could kill C. salmonicolor after 3 sprays of bacterial filtrate. T9 and T6 strains, which were identified by biochemical methods, have similar characteristics to Bacillus thuringiensis

Research of Regenerative Braking Strategy for Electric Vehicles

In the context of global energy instability caused by the transformation of global demand for energy and energy resources, one of the most important areas in the automotive industry is the development of electric vehicles. Serial production of high-tech electric vehicles with a long range contributes to the stabilization of the energy market and the sustainable development of the whole fuel-energy sector. To evaluate the possibility of optimizing the electric vehicles energy consumption, various regenerative braking strategies are discussed in the article based on the Nissan Leaf electric vehicle, which simulation model includes submodules of the traction electric motor, hybrid braking system, traction rechargeable battery and tires. In order to test the adequacy of the simulation model to reproduce the relationship between the operating parameters of electric vehicles various systems and evaluate their ability to regenerate energy during braking the simulation results were compared with the actual experimental data published by the Lab Avt research laboratory (USA). The relative error of the mathematical modeling results of the braking energy regeneration processes is 4.5 %, which indicates the adequacy of the electric vehicle simulation model and the possibility of its using as a base for research and comparison of the energy efficiency of various regenerative braking strategies. As the results of experiments have shown, the usage of the proposed control strategy of the regenerative braking maximum force allows increasing 2.14 times the energy recharging traffic to the battery as compared with the basic control strategy of fixed coefficient braking forces distribution with an increase in braking distance by 10 m. An alternative control strategy of regenerative braking optimal efficiency as compared to the basic control strategy provides a reduction in braking distance by 13.2 % at increasing by 84.4 % the amount of energy generated by the electric motor for recharging the batteries. The carried out investigations confirm the available significant potential for improving the efficiency of the electric vehicles usage by developing the control strategy and algorithms of the braking energy regeneration

Исследование стратегии рекуперативного торможения электромобилей

In the context of global energy instability caused by the transformation of global demand for energy and energy resources, one of the most important areas in the automotive industry is the development of electric vehicles. Serial production of high-tech electric vehicles with a long range contributes to the stabilization of the energy market and the sustainable development of the whole fuel-energy sector. To evaluate the possibility of optimizing the electric vehicles energy consumption, various regenerative braking strategies are discussed in the article based on the Nissan Leaf electric vehicle, which simulation model includes submodules of the traction electric motor, hybrid braking system, traction rechargeable battery and tires. In order to test the adequacy of the simulation model to reproduce the relationship between the operating parameters of electric vehicles various systems and evaluate their ability to regenerate energy during braking the simulation results were compared with the actual experimental data published by the Lab Avt research laboratory (USA). The relative error of the mathematical modeling results of the braking energy regeneration processes is 4.5 %, which indicates the adequacy of the electric vehicle simulation model and the possibility of its using as a base for research and comparison of the energy efficiency of various regenerative braking strategies. As the results of experiments have shown, the usage of the proposed control strategy of the regenerative braking maximum force allows increasing 2.14 times the energy recharging traffic to the battery as compared with the basic control strategy of fixed coefficient braking forces distribution with an increase in braking distance by 10 m. An alternative control strategy of regenerative braking optimal efficiency as compared to the basic control strategy provides a reduction in braking distance by 13.2 % at increasing by 84.4 % the amount of energy generated by the electric motor for recharging the batteries. The carried out investigations confirm the available significant potential for improving the efficiency of the electric vehicles usage by developing the control strategy and algorithms of the braking energy regeneration.В условиях энергетической нестабильности, вызванной трансформацией глобального спроса на энергию и энергоресурсы, одним из важнейших направлений в автомобилестроении является разработка транспортных средств на электрической тяге. Серийное производство высокотехнологичных электромобилей с большим запасом хода способствует стабилизации рынка энергоресурсов и устойчивому развитию всего топливно-энергетического сектора. Для оценки возможности оптимизации энергопотребления электромобилей в статье рассматриваются различные стратегии рекуперативного торможения на базе имитационной модели электромобиля Nissan Leaf, включающей субмодули тягового электродвигателя, гибридной тормозной системы, тяговой аккумуляторной батареи и шин. Результаты моделирования сопоставлялись с экспериментальными данными научно-исследовательской лаборатории Lab Avt (США), опубликованными для проверки адекватности имитационных моделей, воспроизводящих взаимосвязь между рабочими параметрами различных систем электромобиля и оценивающих их способность регенерировать энергию при торможении. Относительная погрешность результатов математического моделирования процессов рекуперации энергии составляет 4,5 %, что свидетельствует об адекватности имитационной модели электромобиля и возможности ее использования в качестве базовой для исследований и сопоставления энергоэффективности различных стратегий рекуперативного торможения. Как показали результаты экспериментов, использование предлагаемой стратегии управления максимальной силой рекуперативного торможения позволяет увеличить трафик энергии подзарядки в 2,14 раза по сравнению с базовой стратегией управления на основе фиксированного коэффициента распределения тормозных усилий по осям транспортного средства при увеличении тормозного пути на 10 м. Альтернативная стратегия управления оптимальной эффективностью рекуперативного торможения обеспечивает по сравнению с базовой стратегией уменьшение тормозного пути на 13,2 % при одновременном увеличении на 84,4 % количества вырабатываемой электродвигателем энергии для подзарядки тяговых аккумуляторных батарей. Проведенные исследования подтверждают имеющийся потенциал по повышению эффективности использования электромобилей за счет совершенствования стратегии и алгоритмов управления рекуперацией энергии торможения