Layer chicken microbiota:a comprehensive analysis of spatial and temporal dynamics across all major gut sections

Background The gut microbiota influences chicken health,welfare,and productivity.A diverse and balanced microbiota has been associated with improved growth,efficient feed utilisation,a well-developed immune system,disease resistance,and stress tolerance in chickens.Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system,under con-trolled research environments,and often sampled at a single time point.To extend these studies,this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks.The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.Results The taxonomic composition of gut microbiota differed significantly between birds in the rearing and pro-duction stages,indicating a shift after laying onset.Similar microbiota compositions were observed between proven-triculus and gizzard,as well as between jejunum and ileum,likely due to their anatomical proximity.Lactobacil-lus dominated the upper gut in pullets and the lower gut in older birds.The oesophagus had a high proportion of Proteobacteria,including opportunistic pathogens such as Gallibacterium.Relative abundance of Gallibacterium increased after peak production in multiple gut sections.Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections.Age influenced microbial richness and diversity in different organs.The upper gut showed decreased diversity over time,possibly influenced by dietary changes,while the lower gut,specifi-cally cecum and colon,displayed increased richness as birds matured.However,age-related changes were inconsist-ent across all organs,suggesting the influence of organ-specific factors in microbiota maturation.Conclusion Addressing a gap in previous research,this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens.This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.

Spatiotemporal Distributions of Bacterioplankton Communities in the Qiantang River(Hangzhou Section),China

The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigation of the bacterioplankton communities in the Qiantang River(Hangzhou section).Water samples were collected quarterly from seven sites over a one-year period.Physical and chemical parameters,including dissolved oxygen(DO),water temperature(WT),chemical oxygen demand(COD),nitrite(NO_(2)^(-)),active phosphate(PO_(4)^(3-))and other indices were determined.In this study,theαdiversity,βdiversity and abundance differences of bacterial communities were investigated using 16S rRNA high-throughput sequencing analysis.The spatial and temporal distribution characteristics and main driving factors of the bacterioplankton community structure and diversity were discussed.The results showed that a total of 57 phyla were detected in the bacterioplankton community,among which Proteobacteria and Actinomycetes were the two dominant groups with the highest relative abundance.The results of PCoA based on Bray-Curtis distance showed that the sampling season had a slightly greater effect on the changes in bacterioplankton community structure in the Qiantang River.Mantel and partial Mantel test showed that environmental variables(Mantel r=0.6739,P<0.0001;partial Mantel r=0.507,P=0.0001)were more important than geographical distance(Mantel r=0.5322,P<0.001;partial Mantel r=0.1563,P=0.001).The distance attenuation model showed that there was significant distance attenuation in all four seasons,and the maximum limit of bacterial community diffusion was found in spring.RDA analysis showed that nine environmental factors,including COD,WT and DO,significantly affected community distribution(P<0.05).This study provides valuable insights into the spatial and temporal distribution characteristics of bacterioplankton communities,shedding light on their ecological roles in the Qiantang River.The information obtained can guide future research on the interactions between bacterioplankton and their environment,enabling the development of effective conservation strategies and sustainable management practices for aquatic ecosystems.

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

VUV imaging of type-ⅠELM filamentary structures and their temporal characteristics on EAST

In the H-mode experiments conducted on the Experimental Advanced Superconducting Tokamak(EAST),fluctuations induced by the so-called edge localized modes(ELMs)are captured by a high-speed vacuum ultraviolet(VUV)imaging system.Clear field line-aligned filamentary structures are analyzed in this work.Ion transport induced by ELM filaments in the scrape-off layer(SOL)under different discharge conditions is analyzed by comparing the VUV signals with the divertor probe signals.It is found that convective transport along open field lines towards the divertor target dominates the parallel ion particle transport mechanism during ELMs.The toroidal mode number of the filamentary structure derived from the VUV images increases with the electron density pedestal height.The analysis of the toroidal distribution characteristics during ELM bursts reveals toroidal asymmetry.The influence of resonance magnetic perturbation(RMP)on the ELM size is also analyzed using VUV imaging data.When the phase difference of the coil changes periodically,the widths of the filaments change as well.Additionally,the temporal evolution of the ELMs on the VUV signals provides rise time and decay time for each single ELM event,and the results indicate a negative correlation trend between these two times.

Elevational and temporal patterns of pollination success in distylous and homostylous buckwheats(Fagopyrum)in the Hengduan Mountains

Reproductive strategies of sexually dimorphic plants vary in response to the environment.Here,we ask whether the sexual systems of Fagopyrum species(i.e.,selfing homostylous and out-crossing distylous)represent distinct adaptive strategies to increase reproductive success in changing alpine environments.To answer this question,we determined how spatial and temporal factors(e.g.,elevation and peak flowering time)affect reproductive success(i.e.,stigmatic pollen load)in nine wild Fagopyrum species(seven distylous and two homostylous)among 28 populations along an elevation gradient of 1299-3315 m in the Hengduan Mountains,southwestern China.We also observed pollinators and conducted hundreds of hand pollinations to investigate inter/intra-morph compatibility,self-compatibility and pollen limitation in four Fagopyrum species(two distylous and two homostylous).We found that Fagopyrum species at higher elevation generally had bigger flowers and more stigmatic pollen loads;lateflowering individuals had smaller flowers and lower pollen deposition.Stigmatic pollen deposition was more variable in distylous species than in homostylous species.Although seed set was not pollenlimited in all species,we found that fruit set was much lower in distylous species,which rely on frequent pollinator visits,than in homostylous species capable of autonomous self-pollination.Our findings that pollination success increases at high elevations and decreases during the flowering season suggest that distylous and homostylous species have spatially and temporally distinct reproductive strategies related to environment-dependent pollinator activity.

The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Spatial compartmentalization and temporal stability of associated microbiota in Pacific oyster Crassostrea gigas

The Pacific oyster Crassostrea gigas,one of the most exploited molluscs in the world,has suffered from massive mortality in recent decades,and the occurrence mechanisms have not been well characterized.In this study,to reveal the relationship of associated microbiota to the fitness of oysters,temporal dynamics of microbiota in the gill,hemolymph,and hepatopancreas of C.gigas during April 2018-January 2019 were investigated by 16 S rRNA gene sequencing.The microbiota in C.gigas exhibited tissue heterogeneity,of which Spirochaetaceae was dominant in the gill and hemolymph while Mycoplasmataceae enriched in the hepatopancreas.Co-occurrence network demonstrated that the gill microbiota exhibited higher inter-taxon connectivity while the hemolymph microbiota had more modules.The richness(Chao 1 index)and diversity(Shannon index)of microbial community in each tissue showed no significant seasonal variations,except for the hepatopancreas having a higher richness in the autumn.Similarly,beta diversity analysis indicated a relatively stable microbiota in each tissue during the sampling period,showing relative abundance of the dominant taxa exhibiting temporal dynamics.Results indicate that the microbial community in C.gigas showed a tissue-specific stability with temporal dynamics in the composition,which might be essential for the tissue functioning and environmental adaption in oysters.This work provides a baseline microbiota in C.gigas and is helpful for the understanding of host-microbiota interaction in oysters.

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

Effect of gas components on the postdischarge temporal behavior of OH and O of a non-equilibrium atmospheric pressure plasma driven by nanosecond voltage pulses

OH radicals and O atoms are two of the most important reactive species of non-equilibrium atmospheric pressure plasma(NAPP),which plays an important role in applications such as plasma medicine.However,experimental studies on how the gas content affects the postdischarge temporal evolutions of OH and O in the noble gas ns-NAPP are very limited.In this work,the effect of the percentages of O_(2),N_(2),and H_(2)O on the amounts of OH and O productions and their post-discharge temporal behaviors in ns-NAPP is investigated by laser-induced fluorescence(LIF)method.The results show that the productions of OH and O increase and then decrease with the increase of O_(2)percentage.Both OH and O densities reach their maximum when about 0.8%O_(2)is added.Further increase of the O_(2)concentration results in a decrease of the initial densities of both OH and O,and leads to their faster decay.The increase of N_(2)percentage also results in the increase and then decrease of the OH and O densities,but the change is smaller.Furthermore,when the H_(2)O concentration is increased from 100 to 3000 ppm,the initial OH density increases slightly,but the OH density decays much faster,while the initial density of O decreases with the increase of the H_(2)O concentration.After analysis,it is found that OH and O are mainly produced through electron collisional dissociation.O(^(1)D)is critical for OH generation.O_(3)accelerates the consumption processes of OH and O at high O_(2)percentage.The addition of H_(2)O in the NAPP considerably enhances the electronegativity,while it decreases the overall plasma reactivity,accelerates the decay of OH,and reduces the O atom density.

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications worldwide.Timely detection is crucial as sepsis can rapidly escalate if left undetected.Recentadvancements in deep learning(DL)offer powerful tools to address this challenge.Aim:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis onset.Implementing the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier predictions.Method:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of sepsis.These time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis onset.All models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical environment.The confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate themodels.Result:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis onset.These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one hour.Implication:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

Efficient User Identity Linkage Based on Aligned Multimodal Features and Temporal Correlation

User identity linkage(UIL)refers to identifying user accounts belonging to the same identity across different social media platforms.Most of the current research is based on text analysis,which fails to fully explore the rich image resources generated by users,and the existing attempts touch on the multimodal domain,but still face the challenge of semantic differences between text and images.Given this,we investigate the UIL task across different social media platforms based on multimodal user-generated contents(UGCs).We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation(EUIL)approach.The method first generates captions for user-posted images with the BLIP model,alleviating the problem of missing textual information.Subsequently,we extract aligned text and image features with the CLIP model,which closely aligns the two modalities and significantly reduces the semantic gap.Accordingly,we construct a set of adapter modules to integrate the multimodal features.Furthermore,we design a temporal weight assignment mechanism to incorporate the temporal dimension of user behavior.We evaluate the proposed scheme on the real-world social dataset TWIN,and the results show that our method reaches 86.39%accuracy,which demonstrates the excellence in handling multimodal data,and provides strong algorithmic support for UIL.

Hierarchical Controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications.Based on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis problem.Second,we develop a dynamic quantization based approach to verify the realization of the accepting path.The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design.Third,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy.Both abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational complexity.Since each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods.Finally,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

Enhancing quantum temporal steering via frequency modulation

Various strategies have been proposed to harness and protect space-like quantum correlations in different models under decoherence.However,little attention has been given to temporal-like correlations,such as quantum temporal steering(TS),in this context.In this work,we investigate TS in a frequency-modulated two-level system coupled to a zero-temperature reservoir in both the weak and strong coupling regimes.We analyze the impact of various frequency-modulated parameters on the behavior of TS and non-Markovian.The results demonstrate that appropriate frequency-modulated parameters can enhance the TS of the two-level system,regardless of whether the system is experiencing Markovian or non-Markovian dynamics.Furthermore,a suitable ratio between modulation strength and frequency(i.e.,all zeroes of the 0th Bessel function J_(0)(δ/?))can significantly enhance TS in the strong coupling regime.These findings indicate that efficient and effective manipulation of quantum TS can be achieved through a frequency-modulated approach.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Deep reinforcement learning using least-squares truncated temporal-difference

Policy evaluation(PE)is a critical sub-problem in reinforcement learning,which estimates the value function for a given policy and can be used for policy improvement.However,there still exist some limitations in current PE methods,such as low sample efficiency and local convergence,especially on complex tasks.In this study,a novel PE algorithm called Least-Squares Truncated Temporal-Difference learning(LST2D)is proposed.In LST2D,an adaptive truncation mechanism is designed,which effectively takes advantage of the fast convergence property of Least-Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning(TD).Then,two feature pre-training methods are utilised to improve the approximation ability of LST2D.Furthermore,an Actor-Critic algorithm based on LST2D and pre-trained feature representations(ACLPF)is proposed,where LST2D is integrated into the critic network to improve learning-prediction efficiency.Comprehensive simulation studies were conducted on four robotic tasks,and the corresponding results illustrate the effectiveness of LST2D.The proposed ACLPF algorithm outperformed DQN,ACER and PPO in terms of sample efficiency and stability,which demonstrated that LST2D can be applied to online learning control problems by incorporating it into the actor-critic architecture.

Prostate cancer temporal and regional trends in Brazil

Objectives:The Brazilian Unified Health System(SistemaÚnico de Saúde−SUS)is the universal public healthcare system of Brazil that maintains a nationwide database of its patients.Our primary objective was to analyze regional and temporal trends,while our secondary goal was to establish correlations between states’health economy status and their prostate cancer(PCa)epidemiology.Methods:We analyzed Brazil’s nationwide data on prostate cancer(PCa)incidence,mortality,and care gathered between 2013 and 2021 by the Information Technology Department of SUS(DATA-SUS),updated monthly using the International Classification of Diseases(ICD-10)code.Results:In the period,273,933 new cases of PCa and 135,336 PCa deaths were reported in men aged 50 years or over in Brazil.The median annual PCa-specific incidence rate(PCSIR)ranged from 14.7 in the Southeast to 6.9 in the North region and the median annual PCa-specific mortality rate(PCSMR)ranged from 7.7 in the Northeast to 6.0 in the South region(per 10,000 men>50).The median annual mortality to incidence ratio(MIR)was highest in the North(0.88)and lowest in the Southeast region(0.44).There were significant regional differences in PCa treatment rates(per new cases);the Midwest region had the highest median annual surgery rate(0.63)while the North region had the highest median annual systemic therapy rate(0.75)and the lowest radiation therapy rate(0.06).Temporal analysis of the data showed significant change in annual rate trends after the year 2018 for PCSIR(coefficient[β]=+3.66,p<0.001),any treatment(β=−0.06,p=0.016),surgery([SR]β=+0.05,p=0.017)radiation therapy([RTR]β=−0.06,p=0.005)and systemic therapy([STR]β=−0.10,p=0.002).After the 2020 pandemic,annual PCSIR decreased(β=−2.15,p=0.002)but annual PCSMR,MIR,and treatment rates remained stable.Correlation studies showed that the PCSIR was strongly negatively correlated with STR(p<0.001)and positively correlated with RTR(p=0.004).MIR was positively correlated with STR(p<0.001)and negatively correlated with the number of robotic surgical systems per million population(p=0.003).Conclusion:Our data shows that PCa care is dependent on the region and is likely influenced by access to treatment options.Furthermore,changes after the year 2018 underscore the influence of international guidelines on Brazilian clinicians’decision-making especially concerning population screening which in turn affected incidence and treatment rates.Limitation of our study includes limited patientrelated information and data on private practices as well as an unknown impact of traveling patients.