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14,330 results on '"bat"'

5. Rabies virus circulation in a highly diverse bat assemblage from a high-risk area for zoonoses outbreaks in the Brazilian Amazon

Author

Novaes, Roberto Leonan M., Almeida, Brunna, Cláudio, Vinícius C., Costa-Neto, Sócrates F., Couto, Ademar Luiz G., Schmidt, Ellen, Bertocchi, Natasha A., Costa, Jéssica Oliveira, Ferreira, Claudio F., de Oliveira, Adi Marcus R., dos Santos, Mara Lucia M., Monteverde, Thays Steffanny D., de Abreu, Elke Maria N., Cunha, Erickson N.P., Borges, Ailton R., Garcia, Jaíne G., Barboza, Camila M., Cocchi, Micheli, Batista, Helena Beatriz C.R., and Moratelli, Ricardo

Published
2024
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13. Virological characteristics of a SARS-CoV-2-related bat coronavirus, BANAL-20-236

Author

Sawa, Hirofumi, Mizuma, Keita, Li, Jingshu, Mimura, Yume, Ohari, Yuma, Tsubo, Tomoya, Ferdous, Zannatul, Shishido, Kenji, Mohri, Hiromi, Iida, Miki, Tsujino, Shuhei, Misawa, Naoko, Usui, Kaoru, Saikruang, Wilaiporn, Lytras, Spyridon, Kawakubo, Shusuke, Nishumura, Luca, Mendoza Tolentino, Jarel Elgin, Li, Wenye, Yo, Maximilian Stanley, Horinaka, Kio, Suganami, Mai, Chiba, Mika, Yoshimura, Ryo, Yasuda, Kyoko, Iida, Keiko, Strange, Adam Patrick, Ohsumi, Naomi, Tanaka, Shiho, Ogawa, Eiko, Okumura, Kaho, Fukuda, Tsuki, Osujo, Rina, Yoshida, Isao, Nakagawa, So, Takaori-Kondo, Akifumi, Shirakawa, Kotaro, Nagata, Kayoko, Nomura, Ryosuke, Horisawa, Yoshihito, Tashiro, Yusuke, Kawai, Yugo, Nakata, Yoshitaka, Futatsusako, Hiroki, Sakamoto, Ayaka, Yasuhara, Naoko, Hashiguchi, Takao, Suzuki, Tateki, Kimura, Kanako, Sasaki, Jiei, Nakajima, Yukari, Yajima, Hisano, Irie, Takashi, Kawabata, Ryoko, Sasaki-Tabata, Kaori, Shimizu, Ryo, Monira Begum, M.S.T., Jonathan, Michael, Mugita, Yuka, Leong, Sharee, Takahashi, Otowa, Ichihara, Kimiko, Ueno, Takamasa, Motozono, Chihiro, Toyoda, Mako, Saito, Akatsuki, Kosaka, Anon, Kawano, Miki, Matsubara, Natsumi, Nishiuchi, Tomoko, Zahradnik, Jiri, Andrikopoulos, Prokopios, Padilla-Blanco, Miguel, Konar, Aditi, Fujita, Shigeru, Plianchaisuk, Arnon, Deguchi, Sayaka, Ito, Hayato, Nao, Naganori, Wang, Lei, Nasser, Hesham, Tamura, Tomokazu, Kimura, Izumi, Kashima, Yukie, Suzuki, Rigel, Suzuki, Saori, Kida, Izumi, Tsuda, Masumi, Oda, Yoshitaka, Hashimoto, Rina, Watanabe, Yukio, Uriu, Keiya, Yamasoba, Daichi, Guo, Ziyi, Hinay, Alfredo A., Jr., Kosugi, Yusuke, Chen, Luo, Pan, Lin, Kaku, Yu, Chu, Hin, Donati, Flora, Temmam, Sarah, Eloit, Marc, Yamamoto, Yuki, Nagamoto, Tetsuharu, Asakura, Hiroyuki, Nagashima, Mami, Sadamasu, Kenji, Yoshimura, Kazuhisa, Suzuki, Yutaka, Ito, Jumpei, Ikeda, Terumasa, Tanaka, Shinya, Matsuno, Keita, Fukuhara, Takasuke, Takayama, Kazuo, and Sato, Kei

Published
2024
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39. Human interactions with bats and bat coronaviruses in rural Côte d'Ivoire

Author

Lange, Christian E., Coulibaly, Julien Kalpy, Ako, Aristide Beranger Ako, N'dri Vakou, Sabine, Koffi, Eugène Kouassi, Mendelsohn, Emma, Ball, Shannon, Martinez, Stephanie, Francisco, Leilani, Saylors, Karen, Manzan, Jean, Bamba, Djeneba, Kouakou, Valère, Koui, Stephane Tossea, Frantz, Jean-Louis, Joly, Damien, Yapi, Cyprien, Daszak, Peter, Dosso, Mireille, and Laudisoit, Anne

Published
2023
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46. A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm.

Author

Pouryosef, Mahrad, Abedini-Nassab, Roozbeh, and Akrami, Seyed Mohammad Reza

Subjects
*ELECTROENCEPHALOGRAPHY, *FEATURE selection, *EPILEPSY, *K-nearest neighbor classification, *SIGNAL classification, *ALGORITHMS, *GENETIC algorithms
Abstract

• For the first time 100% accuracy for epileptic seizure detection using electroencephalogram signals is demonstrated. • Both balanced and unbalanced datasets are studied. • Wavelet extraction from the original EEG signal enhances the feature extraction and selection steps. • Automatic segmentation with algorithmic intelligence is used. • As opposed to Genetic algorithms fast GA is used to achieve fast analysis. The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Mulberry leaf flavonoids activate BAT and induce browning of WAT to improve type 2 diabetes via regulating the AMPK/SIRT1/PGC-1α signaling pathway.

Author

CHENG, Long, SHI, Lu, HE, Changhao, WANG, Chen, LV, Yinglan, LI, Huimin, AN, Yongcheng, DUAN, Yuhui, DAI, Hongyu, ZHANG, Huilin, HUANG, Yan, FU, Wanxin, SUN, Weiguang, and ZHAO, Baosheng

Abstract

Mulberry (Morus alba L.) leaf is a well-established traditional Chinese botanical and culinary resource. It has found widespread application in the management of diabetes. The bioactive constituents of mulberry leaf, specifically mulberry leaf flavonoids (MLFs), exhibit pronounced potential in the amelioration of type 2 diabetes (T2D). This potential is attributed to their ability to safeguard pancreatic β cells, enhance insulin resistance, and inhibit α-glucosidase activity. Our antecedent research findings underscore the substantial therapeutic efficacy of MLFs in treating T2D. However, the precise mechanistic underpinnings of MLF's anti-T2D effects remain the subject of inquiry. Activation of brown/beige adipocytes is a novel and promising strategy for T2D treatment. In the present study, our primary objective was to elucidate the impact of MLFs on adipose tissue browning in db/db mice and 3T3-L1 cells and elucidate its underlying mechanism. The results manifested that MLFs reduced body weight and food intake, alleviated hepatic steatosis, improved insulin sensitivity, and increased lipolysis and thermogenesis in db/db mice. Moreover, MLFs activated brown adipose tissue (BAT) and induced the browning of inguinal white adipose tissue (IWAT) and 3T3-L1 adipocytes by increasing the expressions of brown adipocyte marker genes and proteins such as uncoupling protein 1 (UCP1) and beige adipocyte marker genes such as transmembrane protein 26 (Tmem26), thereby promoting mitochondrial biogenesis. Mechanistically, MLFs facilitated the activation of BAT and the induction of WAT browning to ameliorate T2D primarily through the activation of AMP-activated protein kinase (AMPK)/sirtuin 1 (SIRT1)/peroxisome proliferator-activated receptor-gamma coactivator 1α (PGC-1α) signaling pathway. These findings highlight the unique capacity of MLF to counteract T2D by enhancing BAT activation and inducing browning of IWAT, thereby ameliorating glucose and lipid metabolism disorders. As such, MLFs emerge as a prospective and innovative browning agent for the treatment of T2D. [ABSTRACT FROM AUTHOR]

Published
2023
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