Ribosomal frameshifting at normal codon repeats recodes functional chimeric proteins in human.

Ribosomal frameshifting refers to the process that ribosomes slip into +1 or -1 reading frame, thus produce chimeric trans-frame proteins. In viruses and bacteria, programmed ribosomal frameshifting can produce essential trans-frame proteins for viral replication or regulation of other biological processes. In humans, however, functional trans-frame protein derived from ribosomal frameshifting is scarcely documented. Combining multiple assays, we show that short codon repeats could act as cis-acting elements that stimulate ribosomal frameshifting in humans, abbreviated as CRFS hereafter. Using proteomic analyses, we identified many putative CRFS events from 32 normal human tissues supported by trans-frame peptides positioned at codon repeats. Finally, we show a CRFS-derived trans-frame protein (HDAC1-FS) functions by antagonizing the activities of HDAC1, thus affecting cell migration and apoptosis. These data suggest a novel type of translational recoding associated with codon repeats, which may expand the coding capacity of mRNA and diversify the regulation in human.

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer.

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

State Transition Graph-Based Spatial-Temporal Attention Network for Cell-Level Mobile Traffic Prediction.

Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial-temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the underlying spatial-temporal dynamic information hidden in the historical mobile traffic data. Specifically, we first identify the semantic context information over different segments of the historical data by constructing the state transition graphs, which may reveal different patterns of random fluctuation. Then, based on the state transition graphs, a spatial attention extraction module using graph convolutional networks (GCNs) is designed to aggregate the spatial information of different nodes in the state transition graph. Moreover, a temporal extraction module is employed to capture the dynamic evolution and temporal correlation of the state transition graphs over time. Such a spatial-temporal attention network can be further integrated with a parallel long short-term memory (LSTM) module to improve the accuracy of mobile traffic prediction. Extensive experiments demonstrate that the STG-STAN can better exploit the spatial-temporal information hidden in the state transition graphs, achieving superior performance compared with several baselines.

Mechanism determination on the interactive effects between host immunity and gut microbiome to resist consecutive hydrogen sulfide inhalation of laying hens.

The study aims to investigate the underlying mechanism of the interactions between intestinal microbiota and host immunity-related parameters in response to H2S inhalation of layer hens. A total of 180 healthy 300-day-old Lohmann pink hens with similar body weight were randomly allotted into the control (CON) and the hydrogen sulfide (H2S) treatments for an 8-wk-long feeding procedure. Productive performances, antioxidant capacities, immunity-related parameters, blood metabolites, and cecal microbiota were measured to determine the physiological and gastrointestinal responses to H2S treatment. Results showed that feed intake, egg production, eggshell strength, Haugh unit, and relative yolk weight significantly declined under H2S treatment compared with CON (P < 0.05). Antioxidant and immunity-related parameters showed that glutathione peroxidase, IL-4, and TNF-α contents significantly decreased, whereas contents of IL-1ß, IL-2, and IL-6 significantly increased after H2S treatment (P < 0.05). Further metabolic results showed H2S treatment upregulated 2-mercaptobenzothiazole, D-glucopyranuronic acid, deoxyuridine, cholic acid, and mimosine, etc., which mainly enriched into the pyrimidine metabolism, beta-alanine metabolism, valine, leucine, and isoleucine biosynthesis, and pantothenate and CoA biosynthesis pathways. Meanwhile, aceturic acid, 9-oxodecenoic acid, palmitoleic acid, lauric acid, linoleic acid, oleic acid, and valeric acid mainly contributed to the downregulated metabolites, and enriched into the biosynthesis of unsaturated fatty acids, amino sugar and nucleotide sugar metabolism, tryptophan metabolism and linoleic metabolism. Moreover, H2S treatment significantly proliferated the relative abundances of Faecalibacterium, Ruminococcaceae, and Streptococcus, while decreased Prevotella, Lactobacillus, Bifidobacterium, Clostridium, and Campylobacter (P < 0.05). The altered bacteria were functionally enriched in the carbohydrate metabolism, amino acid metabolism, and metabolism of cofactors and vitamins pathways. H2S treatment also significantly downregulated the expression of ZO-1, Claudin 4, and Claudin 7 (P < 0.05). In summary, intestinal microbial communities altered significantly to make proper adaptations in interacting with the host immune systems through the immunity-related metabolites secretion, and epithelial tight-junction-related genes expressions, purposely to regulate the productive performance under hydrogen sulfide inhalation.

Rumen Microbial Metabolic Responses of Dairy Cows to the Honeycomb Flavonoids Supplement Under Heat-Stress Conditions.

Flavonoids played critical roles in stabilizing microbial homoeostasis when animals suffered exoteric stresses. However, whether flavonoids attenuated heat stress of dairy cows is still not clear. Therefore, in the present article, flavonoids extracted from honeycomb were supplemented to investigate the production, digestibility, and rumen microbial metabolism responses of cows under heat stress conditions. A total of 600 multiparous dairy herds were randomly allotted into the control treatment (CON), the heat stress (HS) treatment, and the honeycomb flavonoids supplement under heat stress conditions (HF) treatment for a 30-day-long trial. Each treatment contains 4 replicates, with 50 cows in each replicate. Production performances including dry matter intake (DMI), milk production, and milk quality were measured on the basis of replicate. Furthermore, two cows of each replicate were selected for the measurement of the nutrient digestibility, the ruminal fermentable parameters including ruminal pH, volatile fatty acids, and ammonia-N, and the rumen microbial communities and metabolism. Results showed that HF effectively increased DMI, milk yield, milk fat, and ruminal acetate content (p < 0.05) compared with HS. Likewise, digestibility of NDF was promoted after HF supplement compared with HS. Furthermore, relative abundances of rumen microbial diversities especially Succiniclasticum, Pseudobutyrivibrio, Acetitomaculum, Streptococcus, and Succinivibrio, which mainly participated in energy metabolism, significantly improved after HF supplement. Metabolomic investigation showed that HF supplement significantly upregulated relative content of lipometabolic-related metabolites such as phosphatidylglycerol, phosphatidylinositol, phosphatidylserine, and phosphatidylethanolamine, while it downregulated biogenic amines. In summary, HF supplement helps proliferate microbial abundances, which further promoted fiber digestibility and energy provision, and ultimately enhances the production performances of dairy cows under heat stress conditions.