Article
Multidisciplinary Sciences
Faizul Hassan, Zhenhua Tang, Hossam M. Ebeid, Mengwei Li, Kaiping Peng, Xin Liang, Chengjian Yang
Summary: The study showed that a herbal mixture at a lower supplemental level of 20g/day can increase milk fat content and unsaturated fatty acids in water buffaloes, without significantly affecting rumen bacterial diversity.
Article
Veterinary Sciences
F. F. Zhao, X. Z. Zhang, Y. Zhang, Mawda Elmhadi, Y. Y. Qin, H. Sun, H. Zhang, M. Z. Wang, H. R. Wang
Summary: This study found that tannic acid-treated corn has significant effects on rumen fermentation characteristics and the composition of ruminal bacterial community in vitro, potentially useful for preventing ruminal acidosis.
FRONTIERS IN VETERINARY SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Min Gao, Adam Cieslak, Haihao Huang, Maciej Gogulski, Daniel Petric, Diana Ruska, Amlan Kumar Patra, Mohamed El-Sherbiny, Malgorzata Szumacher-Strabel
Summary: The objective of this study was to evaluate the effects of replacing raw rapeseed cake with fermented rapeseed cake in the diet of dairy cows. The results showed that fermented rapeseed cake significantly reduced methane production without adversely affecting nutrient digestibility, milk production, and ruminal fermentation.
ANIMAL FEED SCIENCE AND TECHNOLOGY
(2023)
Article
Agriculture, Dairy & Animal Science
P. Papademas, E. Kamilari, M. Aspri, D. A. Anagnostopoulos, P. Mousikos, A. Kamilaris, D. Tsaltas
Summary: The study found that donkey milk has high microbiological quality without significant food-borne pathogens. High-throughput sequencing revealed that donkey milk is mainly composed of gram-negative bacteria including lactic acid bacteria.
JOURNAL OF DAIRY SCIENCE
(2021)
Article
Veterinary Sciences
Tian Xie, Fanlin Kong, Wei Wang, Yajing Wang, Hongjian Yang, Zhijun Cao, Shengli Li
Summary: This study aimed to evaluate the effects of soybean peptides on nutrient degradability, milk production, and rumen bacterial community in dairy cows. The results showed that supplementing soybean peptides improved nutrient degradability, milk production, and antioxidant ability of dairy cows.
FRONTIERS IN VETERINARY SCIENCE
(2022)
Article
Food Science & Technology
Wenhong Zhao, Zhen Liang, Min Qian, Xiangluan Li, Hao Dong, Weidong Bai, Yunlu Wei, Songgui He
Summary: This study investigated the microbial communities in Jiuqu and during fermentation of Chi-flavor type Baijiu using high-throughput sequencing. The results showed that bacteria had greater diversity than fungi in both Jiuqu and during fermentation of Chi-flavor type Baijiu. Lactobacillus and Weissella were the dominant bacteria in Jiuqu, while Lactobacillus, Pediococcus, Lactococcus, Weissella, and Enterobacter were the dominant bacteria in Chi-flavor type Baijiu during fermentation.
LWT-FOOD SCIENCE AND TECHNOLOGY
(2022)
Article
Food Science & Technology
Xinyang Fan, Lihua Qiu, Lige Huang, Wei Zhu, Yongyun Zhang, Yongwang Miao
Summary: Milk protein synthesis is regulated by miRNAs and mRNA genes, with PTHLH playing a crucial role. This study identified differentially expressed miRNA and mRNA genes between early and peak lactation. Overexpression and knockdown experiments of PTHLH gene revealed its ability to promote milk protein synthesis in BuMECs through mTOR and JAK2-STAT5 signaling pathways. Additionally, miR-190a was found to inhibit milk protein synthesis by targeting PTHLH through the same signaling pathways in BuMECs. These findings highlight the significance of the miR-190a-PTHLH pathway in regulating milk protein synthesis.
JOURNAL OF FUNCTIONAL FOODS
(2023)
Article
Biochemistry & Molecular Biology
Xiaochun Zheng, Xiaoyu Xu, Yanqing Ma, Lihua Zhu, Jing Xiao, Li Deng, Xuewei Shi, Bin Wang
Summary: This study utilized high-throughput sequencing technology to explore the microbial diversity during the milk fermentation of Kazak traditional hand-fermented cheese, identifying key microbial groups. The research highlighted the high enzyme activity characteristics of Lactobacillus and Lactococcus in cheese, shedding light on their important roles in fermentation.
PROCESS BIOCHEMISTRY
(2021)
Article
Agronomy
Sonny C. Ramos, Chang-Dae Jeong, Lovelia L. Mamuad, Seon-Ho Kim, A-Rang Son, Michelle A. Miguel, Mahfuzul Islam, Yong-Il Cho, Sang-Suk Lee
Summary: This study investigated the effects of rumen buffer agents on ruminal fermentation parameters and bacterial community composition using in vitro and in vivo experiments. The buffer agents were found to stabilize ruminal pH, improve fermentation, and alter bacterial community, potentially preventing ruminal acidosis induced by high-concentrate diets in dairy cows.
Review
Microbiology
Marcio Vargas-Ramella, Mirian Pateiro, Aristide Maggiolino, Michele Faccia, Daniel Franco, Pasquale De Palo, Jose M. Lorenzo
Summary: Probiotics have been widely used in the food industry in the past two decades, with a growing focus on their health benefits for consumers. Strategies need to be developed to increase the consumption of functional foods to meet criteria for probiotic usefulness and the demands of the consumer market.
Article
Agriculture, Dairy & Animal Science
Jagoba Rey, Xabier Diaz de Otalora, Raquel Atxaerandio, Nerea Mandaluniz, Aser Garcia-Rodriguez, Oscar Gonzalez-Recio, Adrian Lopez-Garcia, Roberto Ruiz, Idoia Goiri
Summary: This study used whole-metagenome sequencing to investigate the effects of chitosan on rumen microbial taxonomy and methanogenesis. The results showed that chitosan did not affect microbial diversity but induced shifts in the relative abundance of certain microbial taxa. However, chitosan supplementation did not have any significant impact on CH4 emissions, microbial protein synthesis, and productive performance.
Article
Agriculture, Dairy & Animal Science
Ahmed E. Kholif, Hatem A. Hamdon, Gouda A. Gouda, Ayman Y. Kassab, Tarek A. Morsy, Amlan K. Patra
Summary: Ensiling with fibrolytic enzymes or lactic-acid bacteria can enhance the nutritive value of date palm leaves, resulting in improved feed efficiency and lactational performance of ewes.
Article
Multidisciplinary Sciences
Felipe Senne de Oliveira Lino, Djordje Bajic, Jean Celestin Charles Vila, Alvaro Sanchez, Morten Otto Alexander Sommer
Summary: This study dissects the microbial interactions in sugarcane ethanol fermentation, showing that higher-order interactions can counterbalance the negative effects of pairwise interactions. It is found that Lactobacillus amylovorus improves yeast growth rate and ethanol yield by cross-feeding acetaldehyde. Overall, the results suggest that comprehensive study of microbial communities is biotechnologically important for improving fermentation yields.
NATURE COMMUNICATIONS
(2021)
Article
Engineering, Chemical
Mikhail Y. Syromyatnikov, Ekaterina Y. Nesterova, Maria Gladkikh, Anna A. Tolkacheva, Olga Bondareva, Vladimir M. Syrov, Nina A. Pryakhina, Vasily N. Popov
Summary: This study demonstrates the use of high-throughput sequencing for quality control and safety evaluation of microbial bioformulations, revealing discrepancies between the actual bacterial composition and what is declared by the manufacturers in some samples.
Article
Agriculture, Dairy & Animal Science
Zichen Wang, Kaifeng Niu, Hossam E. Rushdi, Mingyue Zhang, Tong Fu, Tengyun Gao, Liguo Yang, Shenhe Liu, Feng Lin
Summary: This study explored the metabolic mechanism of rumen bacteria in buffalo under heat stress using 16S rDNA technology and metabolome analysis. The findings showed that heat stress increased respiratory rate and skin temperature, and decreased rumen volatile fatty acids. Changes were observed in the structures of rumen bacterial communities and a total of 32 metabolites involved in gluconeogenesis were altered.
Article
Computer Science, Artificial Intelligence
Liang Tan, Keping Yu, Ali Kashif Bashir, Xiaofan Cheng, Fangpeng Ming, Liang Zhao, Xiaokang Zhou
Summary: This paper proposes a real-time cardiovascular monitoring system using 5G and deep learning to predict the cardiovascular health of COVID-19 patients. Experimental results show that the proposed system can address technical limitations and improve the prediction accuracy of cardiovascular disease.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, H. Vincent Poor
Summary: This paper discusses the problem of executing a task from a quantized version of the information source. The task is modeled by minimizing a general goal function with quantized parameters. The paper shows how to design a quantizer to minimize the gap between the quantized version and the optimal result. The analysis provides quantization strategies and allows a practical algorithm to be designed.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ju-Hyung Lee, Hyowoon Seo, Jihong Park, Mehdi Bennis, Young-Chai Ko
Summary: This paper proposes a novel contention-based random access solution for low Earth orbit satellite (LEO SAT) networks, called eRACH, which achieves automatic protocol establishment through multi-agent deep reinforcement learning in a non-stationary network environment. In contrast to existing model-based and standardized protocols, eRACH does not require central coordination or additional communication across users, and training convergence is stabilized through regular orbiting patterns. Compared to RACH, simulation results show that eRACH achieves 54.6% higher average network throughput, around two times lower average access delay, and a Jain's fairness index of 0.989.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Won Joon Yun, Yunseok Kwak, Hankyul Baek, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim
Summary: This paper proposes a novel learning framework by integrating Federated Learning (FL) with width-adjustable slimmable neural networks (SNNs) to address the challenges posed by heterogeneous energy, wireless channel conditions, and non-IID data distributions. The proposed method, named SlimFL, utilizes superposition coding (SC) and superposition training (ST) to achieve communication and energy efficiency in global model aggregation and local model updating. Formal proofs and data-intensive simulations demonstrate that SlimFL is capable of dealing with non-IID data distributions and poor channel conditions while maintaining high communication efficiency.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2023)
Article
Entomology
Sheng-Yu Zhang, Han Gao, Ankarjan Askar, Xing-Peng Li, Guo-Cai Zhang, Tian-Zhong Jing, Hang Zou, Hao Guan, Yun-He Zhao, Chuan-Shan Zou
Summary: This study reveals that the steroid hormone 20-hydroxyecdysone (20E) disrupts lipid metabolism in the fat body of Hyphantria cunea larvae, accelerating fatty acid beta-oxidation and promoting lipolysis. However, it negatively regulates gluconeogenesis.
Article
Agriculture, Dairy & Animal Science
Muhammad Wasim Iqbal, Ina Draganova, Patrick Charles Henry Morel, Stephen Todd Morris
Summary: This study investigated the variations in grazing, rumination, and idling behaviors by grazing dairy cows over 24 h. The cows spent most of the daytime grazing and most of the nighttime ruminating, with a short grazing period around midnight. The cows adjusted their grazing patterns according to varying day lengths during different seasons and finished grazing earlier when received supplementary feeds. These findings could have implications to improve pasture management efficiency, additional feed demand, and animal welfare in varying environmental conditions.
JOURNAL OF ANIMAL SCIENCE
(2023)
Article
Automation & Control Systems
Veronica Toro-Betancur, Gopika Premsankar, Chen-Feng Liu, Mariusz Slabicki, Mehdi Bennis, Mario Di Francesco
Summary: LoRa is a popular technology for low-power wide area networks, offering long-range communication with low energy consumption. Existing methods have not addressed the issue of parameter reassignment when channel conditions change. To address this, the article proposes a distributed game-theoretic approach called NoReL, which allows nodes to autonomously update parameters and maximize packet delivery ratio.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Ju-Hyung Lee, Jihong Park, Mehdi Bennis, Young-Chai Ko
Summary: This study investigates the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using RF or FSO link within a NTN. The associations with orbiting SATs and the trajectories of UAVs are optimized to maximize communication efficiency. A multi-agent deep reinforcement learning approach with action dimensionality reduction technique is proposed to overcome the challenges. Simulation results show that the SAT-UAV integrated scheme achieves higher end-to-end sum throughput and energy efficiency compared to benchmark schemes, and the proposed scheme utilizing hybrid FSO/RF links achieves significantly higher peak and worst-case throughput, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5 G NTNs.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Gilsoo Lee, Walid Saad, Mehdi Bennis, Cheonyong Kim, Minchae Jung
Summary: In this paper, the problem of ephemeral edge computing in IoT is studied, and a novel online framework is proposed to optimize task allocation and computation in a limited time period. By considering communication and computation latency, the proposed framework maximizes the number of computed tasks and solves the joint optimization problem using an online greedy algorithm. Simulation results demonstrate that the proposed online algorithm achieves near-optimal task allocation with an optimality gap no higher than 7.1% compared to the offline, optimal solution with complete task knowledge.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Telecommunications
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush, Mehdi Bennis
Summary: This paper proposes a novel framework for analyzing the energy and carbon footprints in distributed and federated learning. It discusses the impact of communication efficiency and learner population size on the sustainability of distributed learning through two case studies.
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
(2023)
Article
Telecommunications
Jin-Hyun Ahn, Mehdi Bennis, Joonhyuk Kang
Summary: Recently, it has been shown that analog transmission based federated learning is more efficient in using communication resources compared to digital transmission. This paper proposes an effective model compression strategy for analog federated learning under constrained communication bandwidth. The strategy is based on pattern shared sparsification, where the same sparsification pattern is applied to parameter vectors uploaded by edge devices. Specific schemes for determining the sparsification pattern are proposed and the convergence of analog federated learning leveraging these schemes is characterized by deriving a closed-form upper bound of convergence rate and residual error. The numerical results demonstrate consistent performance improvement of analog federated learning with pattern shared sparsification, especially under scarce communication bandwidth and limited transmit power budget.
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
(2023)
Article
Engineering, Electrical & Electronic
Sejin Seo, Jihong Park, Seung-Woo Ko, Jinho Choi, Mehdi Bennis, Seong-Lyun Kim
Summary: This article proposes a semantic protocol model (SPM) that transforms a neural network (NN) protocol model into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). Extensive simulations show that the SPM closely approximates the original NN model while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, the SPM enables various applications such as SPM reconfiguration for collision-avoidance, comparing different SPMs via semantic entropy calculation, and storing multiple SPMs to cope with non-stationary environments.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Microbiology
Mary E. Crompton, Luca F. Gaessler, Patrick O. Tawiah, Lisa Polzer, Sydney K. Camfield, Grady D. Jacobson, Maren K. Naudszus, Colton Johnson, Kennadi Meurer, Mehdi Bennis, Brendan Roseberry, Sadia Sultana, Jan-Ulrik Dahl
Summary: In this study, the researchers identified a novel defense strategy, the RcrR regulon, in uropathogenic Escherichia coli (UPEC), which protects the bacteria from the antimicrobial oxidant hypochlorous acid (HOCl). They found that the expression of the rcrARB operon, controlled by the HOCl-sensing transcriptional repressor RcrR, plays a crucial role in protecting UPEC from HOCl. The deletion of the rcrB gene, encoding a hypothetical membrane protein, increased UPEC's susceptibility to HOCl. The researchers also investigated the mechanism behind RcrB's protection and found that RcrB expression is induced by and protects from several reactive chlorine species (RCS) but not reactive oxygen species (ROS). RcrB plays a protective role for RCS-stressed planktonic cells under various growth and cultivation conditions, but does not seem to be relevant for UPEC's biofilm formation.
JOURNAL OF BACTERIOLOGY
(2023)
Proceedings Paper
Computer Science, Hardware & Architecture
Mounssif Krouka, Anis Elgabli, Chaouki ben Issaid, Mehdi Bennis
Summary: In this paper, a decentralized Newton-type approach is proposed to solve the problem of decentralized federated learning. The algorithm leverages the fast convergence of second-order methods and reduces communication and privacy concerns. The approach consists of solving an inner problem and an outer problem alternately using a decentralized manner and performing one decentralized Newton step at every iteration. Simulation results demonstrate that the proposed algorithm outperforms several baselines and provides efficient solutions for bandwidth-limited systems under different SNR regimes.
2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC
(2023)
Review
Computer Science, Information Systems
Andrea Zanella, Sergio Zubelzu, Mehdi Bennis
Summary: This article discusses the fundamental role of sensing technologies, data processing algorithms, and inference based on machine learning techniques in modern hydrology. It also highlights the challenges in improving the accuracy and reducing the complexity of current hydrology models. The article provides an overview of the main solutions proposed in the literature and presents empirical data sets to support the main concepts discussed.