Federated Graph Anomaly Detection via Contrastive Self-Supervised Learning.

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to anomaly detection tasks in distributed environments may lead to suboptimal performance results. We propose a federated graph anomaly detection framework via contrastive self-supervised learning (CSSL) federated CSSL anomaly detection framework (FedCAD) to address these challenges. FedCAD updates anomaly node information between clients via federated learning (FL) interactions. First, FedCAD uses pseudo-label discovery to determine the anomaly node of the client preliminarily. Second, FedCAD employs a local anomaly neighbor embedding aggregation strategy. This strategy enables the current client to aggregate the neighbor embeddings of anomaly nodes from other clients, thereby amplifying the distinction between anomaly nodes and their neighbor nodes. Doing so effectively sharpens the contrast between positive and negative instance pairs within contrastive learning, thus enhancing the efficacy and precision of anomaly detection through such a learning paradigm. Finally, the efficiency of FedCAD is demonstrated by experimental results on four real graph datasets.

Shape Sensing and Kinematic Control of a Cable-Driven Continuum Robot Based on Stretchable Capacitive Sensors.

A Cable-Driven Continuum Robot (CDCR) that consists of a set of identical Cable-Driven Continuum Joint Modules (CDCJMs) is proposed in this paper. The CDCJMs merely produce 2-DOF bending motions by controlling driving cable lengths. In each CDCJM, a pattern-based flexible backbone is employed as a passive compliant joint to generate 2-DOF bending deflections, which can be characterized by two joint variables, i.e., the bending direction angle and the bending angle. However, as the bending deflection is determined by not only the lengths of the driving cables but also the gravity and payload, it will be inaccurate to compute the two joint variables with its kinematic model. In this work, two stretchable capacitive sensors are employed to measure the bending shape of the flexible backbone so as to accurately determine the two joint variables. Compared with FBG-based and vision-based shape-sensing methods, the proposed method with stretchable capacitive sensors has the advantages of high sensitivity to the bending deflection of the backbone, ease of implementation, and cost effectiveness. The initial location of a stretchable sensor is generally defined by its two endpoint positions on the surface of the backbone without bending. A generic shape-sensing model, i.e., the relationship between the sensor reading and the two joint variables, is formulated based on the 2-DOF bending deflection of the backbone. To further improve the accuracy of the shape-sensing model, a calibration method is proposed to compensate for the location errors of stretchable sensors. Based on the calibrated shape-sensing model, a sliding-mode-based closed-loop control method is implemented for the CDCR. In order to verify the effectiveness of the proposed closed-loop control method, the trajectory tracking accuracy experiments of the CDCR are conducted based on a circle trajectory, in which the radius of the circle is 55mm. The average tracking errors of the CDCR measured by the Qualisys motion capture system under the open-loop and the closed-loop control are 49.23 and 8.40mm, respectively, which is reduced by 82.94%.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction.

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

Diffusion models in text generation: a survey.

Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text outputs. In this article, we conduct a comprehensive survey on the application of diffusion models in text generation. We divide text generation into three parts (conditional, unconstrained, and multi-mode text generation, respectively) and provide a detailed introduction. In addition, considering that autoregressive-based pre-training models (PLMs) have recently dominated text generation, we conduct a detailed comparison between diffusion models and PLMs in multiple dimensions, highlighting their respective advantages and limitations. We believe that integrating PLMs into diffusion is a valuable research avenue. We also discuss current challenges faced by diffusion models in text generation and propose potential future research directions, such as improving sampling speed to address scalability issues and exploring multi-modal text generation. By providing a comprehensive analysis and outlook, this survey will serve as a valuable reference for researchers and practitioners interested in utilizing diffusion models for text generation tasks.

Association between social isolation and depression: Evidence from longitudinal and Mendelian randomization analyses.

BACKGROUND: Increasing evidence shows that social isolation and depression are likely to interact with each other, yet the direction and causality of the association are not clear. This study aims to examine the possible reciprocity in the relationship between social isolation and depression. METHODS: This study fitted a cross-lagged panel model (CLPM) by using data from the English Longitudinal Study of Aging (ELSA, 2014-2019, n = 6787) to examine the temporal relationship between social isolation and depressive symptoms in older adults. We then conducted two-sample bidirectional Mendelian randomization (MR) analyses by using independent genetic variants associated with multiple social isolation phenotypes (n = 448,858-487,647) and with depression (n = 215,644-2,113,907) as genetic instruments from genome-wide association studies to assess the causality between social isolation and onset of depression. RESULTS: The CLPM in the ELSA cohort showed a significant and positive lagged effect of social isolation on depressive symptoms (ß = 0.037, P < .001). The reverse cross-lagged path from depressive symptoms to social isolation was also statistically significant (ß = 0.039, P < .001). In two-sample bidirectional MR, the genetically predicted loneliness and social isolation combined phenotype (LNL-ISO) was positively associated with occurrence of depression (OR = 1.88, 95 % CI: 1.41-2.50, P < .001), vice versa (OR = 1.16, 95 % CI:1.13-1.20, P < .001). LIMITATIONS: The self-report nature of the assessments and missing data are study limitations. CONCLUSIONS: These findings suggest a bidirectional relationship between social isolation and depression. It is important to develop interventions that highlight the reciprocal consequences of improving either mental health or social connection in older adults.

Integrated genetic and epigenetic analyses uncovered GLP1R association with metabolically healthy obesity.

BACKGROUND: Both genetic and epigenetic variations of GLP1R influence the development and progression of obesity. However, the underlying mechanism remains elusive. This study aims to explore the mediation roles of obesity-related methylation sites in GLP1R gene variants-obesity association. METHODS: A total of 300 Chinese adult participants were included in this study and classified into two groups: 180 metabolically healthy obesity (MHO) cases and 120 metabolically healthy normal-weight (MHNW) controls. Questionnaire investigation, physical measurement and laboratory examination were assessed in all participants. 18 single nucleotide polymorphisms (SNPs) and 31 CpG sites were selected for genotype and methylation assays. Causal inference test (CIT) was performed to evaluate the associations between GLP1R genetic variation, DNA methylation and MHO. RESULTS: The study found that rs4714211 polymorphism of GLP1R gene was significantly associated with MHO. Additionally, methylation sites in the intronic region of GLP1R (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 12.13; GLP1R-68-CpG 17; GLP1R-68-CpG 21) were associated with MHO, and two of these methylation sites (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 17) partially mediated the association between genotypes and MHO. CONCLUSIONS: Not only the gene polymorphism, but also the DNA methylation of GLP1R was associated with MHO. Epigenetic changes in the methylome may in part explain the relationship between genetic variants and MHO.

Association of Abnormal Sleep Duration and Sleep Disturbance with Physical Activity in Older Adults: Between- and within-Person Effects.

OBJECTIVES: Sleep is associated with physical activity (PA), yet the nature and directions of this association are less understood. This study aimed to disentangle the long-term temporal sequences between sleep duration/disturbance and PA in older adults, distinguishing between- and within-person effects. DESIGN: Longitudinal panel study. SETTING AND PARTICIPANTS: We conducted a longitudinal study using 3 waves of data collected in 2008/09 (T1), 2012/13 (T2), and 2016/17(T3) from adults aged ≥50 years in the English Longitudinal Study of Ageing (N = 10,749 individuals). MEASURES: Sleep duration, sleep disturbance, and PA were assessed by self-reported questionnaires. We used cross-lagged panel models (CLPMs) to examine between-person effects and random intercept cross-lagged panel models (RI-CLPMs) to examine within-person effects. RESULTS: Our analyses revealed a reciprocal relationship between abnormal sleep duration and low PA levels at between-person level (abnormal sleep duration to PA: ßT1-T2 = -0.053, ßT2-T3 = -0.058, all P < .001; PA to abnormal sleep duration: ßT1-T2 = -0.040, ßT2-T3 = -0.045, all P < .05), with abnormal sleep duration being the driving force in the dynamic association. In addition, there was a unidirectional effect of more severe sleep disturbance on lower levels of PA at both between- and within-person levels (between-person level: ßT1-T2 = -0.032, ßT2-T3 = -0.028, all P < .001; within-person level: ßT1-T2 and T2-T3 = -0.031, all P = .011). CONCLUSIONS AND IMPLICATIONS: This study adds novel insights into the temporal directionality of sleep and PA among community-dwelling older adults and highlights poor sleep as a potential risk factor for PA. Intervention strategies should aim to improve sleep to promote PA levels and successful aging.

FedLGAN: a method for anomaly detection and repair of hydrological telemetry data based on federated learning.

The existing data repair methods primarily focus on addressing missing data issues by utilizing variational autoencoders to learn the underlying distribution and generate content that represents the missing parts, thus achieving data repair. However, this method is only applicable to data missing problems and cannot identify abnormal data. Additionally, as data privacy concerns continue to gain public attention, it poses a challenge to traditional methods. This article proposes a generative adversarial network (GAN) model based on the federated learning framework and a long short-term memory network, namely the FedLGAN model, to achieve anomaly detection and repair of hydrological telemetry data. In this model, the discriminator in the GAN structure is employed for anomaly detection, while the generator is utilized for abnormal data repair. Furthermore, to capture the temporal features of the original data, a bidirectional long short-term memory network with an attention mechanism is embedded into the GAN. The federated learning framework avoids privacy leakage of hydrological telemetry data during the training process. Experimental results based on four real hydrological telemetry devices demonstrate that the FedLGAN model can achieve anomaly detection and repair while preserving privacy.

HDAC6 decreases H4K16 and α-tubulin acetylation during porcine oocyte maturation.

HDAC6 is an essential factor in mouse oocyte maturation. However, the roles of HDAC6 in porcine oocyte maturation are still unclear. Therefore, we analyzed the roles of HDAC6 in porcine oocyte maturation by treatment with Tubastatin A (TubA) which is an HDAC6 inhibitor. Our results showed that treatment with 10 µg/ml TubA significantly decreased the rate of porcine oocyte maturation, but it did not influence the rate of germinal vesicle breakdown (GVBD). Then, we found that TubA treatment disrupted spindle organization by increasing the α-tubulin acetylation level during porcine oocyte maturation. Moreover, TubA treatment significantly increased H4K16 acetylation, which may compromise kinetochore and microtubule (K-MT) attachment during meiosis in porcine oocytes. We also analyzed the effects of TubA on meiosis-related (H3T3pho and H3S10pho) and transcription-related histone modifications (H3K4me3, H3K9me3 and H3K4ac) during porcine oocyte maturation. The results showed that TubA treatment increased H3S10pho and H3K4ac levels, but no influence was seen in H3T3pho, H3K4me3 and H3K9me3 levels in porcine oocytes. TubA treated oocytes also showed a compromised ability to develop after parthenogenetic activation. Finally, we found that HDAC6 exhibited higher mRNA levels and lower DNA methylation levels in porcine oocytes than it did in porcine embryonic fibroblasts (PEFs). These results indicate that the low level of DNA methylation in HDAC6 promoter ensures high expression. HDAC6 regulates the deacetylation of α-tubulin and H4K16, which promotes correct spindle organization and meiotic apparatus assembly during porcine oocyte maturation. This study illustrates a new pathway by which HDAC6 modulates mammalian oocyte maturation.

Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study.

Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05-1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.

Spatial-temporal hypergraph convolutional network for traffic forecasting.

Accurate traffic forecasting plays a critical role in the construction of intelligent transportation systems. However, due to the across road-network isomorphism in the spatial dimension and the periodic drift in the temporal dimension, existing traffic forecasting methods cannot satisfy the intricate spatial-temporal characteristics well. In this article, a spatial-temporal hypergraph convolutional network for traffic forecasting (ST-HCN) is proposed to tackle the problems mentioned above. Specifically, the proposed framework applies the K-means clustering algorithm and the connection characteristics of the physical road network itself to unify the local correlation and across road-network isomorphism. Then, a dual-channel hypergraph convolution to capture high-order spatial relationships in traffic data is established. Furthermore, the proposed framework utilizes a long short-term memory network with a convolution module (ConvLSTM) to deal with the periodic drift problem. Finally, the experiments in the real world demonstrate that the proposed framework outperforms the state-of-the-art baselines.

Genome-wide DNA methylation analysis of body composition in Chinese monozygotic twins.

BACKGROUND: Little is currently known about epigenetic alterations associated with body composition in obesity. Thus, we aimed to explore epigenetic relationships between genome-wide DNA methylation levels and three common traits of body composition as measured by body fat percentage (BF%), fat mass (FM) and lean body mass (LBM) among Chinese monozygotic twins. METHODS: Generalized estimated equation model was used to regress the methylation level of CpG sites on body composition. Inference about Causation Through Examination Of Familial Confounding was used to explore the evidence of a causal relationship. Gene expression analysis was further performed to validate the results of differentially methylated genes. RESULTS: We identified 32, 22 and 28 differentially methylated CpG sites (p < 10-5 ) as well as 20, 17 and eight differentially methylated regions (slk-corrected p < 0.05) significantly associated with BF%, FM and LBM which were annotated to 65 genes, showing partially overlapping. Causal inference demonstrated bidirectional causality between DNA methylation and body composition (p < 0.05). Gene expression analysis revealed significant correlations between expression levels of five differentially methylated genes and body composition (p < 0.05). CONCLUSIONS: These DNA methylation signatures will contribute to increased knowledge about the epigenetic basis of body composition and provide new strategies for early prevention and treatment of obesity and its related diseases.

Serial multiple mediation of loneliness and depressive symptoms in the relationship between pain and cognitive function among older people.

OBJECTIVES: Although the negative effect of pain on cognitive function has been widely reported, it is unclear how the effect is mediated. The aim of this study is to analyze the mediating role of loneliness and depressive symptoms in the association between pain and cognitive function. METHODS: A total of 6,309 participants aged ≥50 years from 2012/13 (T1), 2014/15 (T2), 2016/17 (T3) and 2018/19 (T4) of the English Longitudinal Study of Aging (ELSA) were included. Of them, 55.8% were females, and the median age (rang) was 65 (50-99) years at T1. Serial mediation analysis was performed using Mplus 8.3. RESULTS: The mediation model explained 10.1% of the variance in loneliness, 22.1% of the variance of depressive symptoms, and 22.7% of the variance of cognitive function. Higher level pain was associated with poorer cognitive function (c: ß = -0.057; p < 0.001). The negative effect of pain on cognition was mediated separately and sequentially through loneliness and depressive symptoms, with loneliness and depressive symptoms explaining 8.8% of the total effect, respectively, and the pathway of loneliness and subsequent depression explaining 1.8%. CONCLUSIONS: Diversified interventions aimed at treating pain in older adults would be beneficial for their mental health and cognitive function.

Transcription factor ELK1 regulates the expression of histone 3 lysine 9 to affect developmental potential of porcine preimplantation embryos.

A series of changes occur in the early embryo that are critical for subsequent development, and the pig is an excellent animal model of human disease, so understanding the regulatory mechanisms of early embryonic development in the pig is of very importance. To find key transcription factors regulating pig early embryonic development, we first profiled the transcriptome of pig early embryos, and confirmed that zygotic gene activation (ZGA) in porcine embryos starts from 4 cell stage. Subsequent enrichment analysis of up-regulated gene motifs during ZGA revealed that the transcription factor ELK1 ranked first. The expression pattern of ELK1 in porcine early embryos was analyzed by immunofluorescence staining and qPCR, and the results showed that the transcript level of ELK1 reached the highest at the 8 cell stage, while the protein level reached the highest at 4 cell stage. To further investigate the effect of ELK1 on early embryo development in pigs, we silenced ELK1 in zygotes and showed that ELK1 silencing significantly reduced cleavage rate, blastocyst rate as well as blastocyst quality. A significant decrease in the expression of the pluripotency gene Oct4 was also observed in blastocysts from the ELK1 silenced group by immunofluorescence staining. Silencing of ELK1 also resulted in decreased H3K9Ac modification and increased H3K9me3 modification at 4 cell stage. To investigate the effect of ELK1 on ZGA, we analyzed transcriptome changes in 4 cell embryos after ELK1 silencing by RNA seq, which revealed that ELK1 silencing resulted in significant differences in the expression of a total of 1953 genes at the 4 cell stage compared with their normal counterparts, including 1106 genes that were significantly upregulated and 847 genes that were significantly downregulated. Through GO and KEGG enrichment, we found that the functions and pathways of down-regulated genes were concentrated in protein synthesis, processing, cell cycle regulation, etc., while the functions of up-regulated genes were focused on aerobic respiration process. In conclusion, this study demonstrates that the transcription factor ELK1 plays an important role in regulation of preimplantation embryo development of pigs and deficiency of ELK1 leads to abnormal epigenetic reprogramming as well as zygotic genome activation, thus adversely affecting embryonic development. This study will provide important reference for the regulation of transcription factors in porcine embryo development.

Bidirectional longitudinal associations between balance performance and depressive symptoms in older adults: A cross-lagged panel model.

BACKGROUND: Evidence on the temporal sequences between balance and depressive symptoms is limited, and no studies have compared the strength of each direction. This study aimed to assess the association between balance performance and depressive symptoms among community-dwelling older adults, and further to explore the driving factors in the dynamic association. METHODS: Data were obtained from the English Longitudinal Study of Aging (ELSA). Overall, 3971 community-residing adults aged 50 years or older were assessed at 2004/05, 2008/09, and 2012/13. Balance was measured using three progressively more difficult tasks (side-by-side, semi-tandem, and full-tandem). Depressive symptoms were determined with a dichotomous eight-item version of the Center for Epidemiologic Studies Depression Scale (CES-D). Cross-lagged panel models were used to test the reciprocal relationships between balance and depressive symptoms. RESULTS: Our analyses revealed that earlier poorer balance predicted later worse depressive symptoms consistently across waves (ßW2-W4 = -0.058, P < .05, ßW4-W6 = -0.067, P < .001). Conversely, the higher scores of depressive symptoms at wave 4 predicted lower level of balance at wave 6 (ßW4-W6 = -0.038, P = .018). The cross-lagged effects of balance on depressive symptoms were over all stronger than the reverse effects. CONCLUSIONS: These findings add novel insights into the temporal directionality of balance and depressive symptoms among community-dwelling older adults, and suggest that a predominance of balance disorder effects. Interventional strategy should aim to increase balance ability from earlier stages to promote successful aging.

Association of diabetes mellitus with gait and falls in community-dwelling older adults: Serial mediation of vision and cognition.

BACKGROUND/OBJECTIVE: Diabetes mellitus (DM) is associated with walking performance, but potential underlying mechanisms of this association remain unclear. The present study aims to disentangle the pathways linking DM to gait and falls through the serial mediation effect of vision and cognition among the older population. METHODS: Data were taken from wave 9 (2018-2019) of the English Longitudinal Study of Aging (ELSA), including 5496 participants aged 60 years and older. DM was identified based on medical diagnosis and laboratory tests. Vision and falls were self-reported. Cognition was evaluated using questionnaire. Gait speed was measured by the "timed walking test". Serial mediation analysis was performed using Mplus 8.3. RESULTS: DM was associated with impaired gait speed (c = 0.085, P < 0.05) and falls (c = 0.061, P < 0.05). The serial mediation model revealed that vision and cognition significantly mediated the association of DM with impaired gait speed, with 17.97% and 23.60% of the total effects explained by vision and cognition respectively, and 3.37% explained by the path through vision and then cognition. Similarly, vision and cognition significantly mediated the association of DM with falls, with 14.99% and 6.67% of the total effects explained by vision and cognition respectively, and 1.67% explained by the path through vision and then cognition. CONCLUSIONS: These findings contribute to deeper understanding of the mechanism underlying the association of DM with walking performance. Evaluation and intervention targeted at vision and the cognition may be beneficial for improving gait or reducing falls in older adults with DM.

LncRNA affects epigenetic reprogramming of porcine embryo development by regulating global epigenetic modification and the downstream gene SIN3A.

The study of preimplantation development is of great significance to reproductive biology and regenerative medicine. With the development of high-throughput deep sequencing technology, it has been found that lncRNAs play a very important role in the regulation of embryonic development. In this study, key lncRNAs that regulate embryonic development were screened by analyzing the expression pattern of lncRNAs in porcine in vivo fertilization (IVV) embryos. By knocking down lncRNA expression in in vitro fertilization (IVF) embryos, we investigated its function and mechanism of regulating embryonic development. The results showed that the expression pattern of lncRNA was consistent with the time of gene activation. The lncRNAs were highly expressed in the 4-cell to blastocyst stage but barely expressed in the oocytes and 2-cell stage. So we speculated this part of lncRNAs may regulate gene expression. The lncRNA LOC102165808 (named lncT because the gene near this lncRNA is TFAP2C) was one of them. The knockdown (KD) of lncT inhibited embryonic development, resulting in decreased H3K4me3, H3K4me2, and H3K9me3, and increased DNA methylation. Meanwhile, RNAseq showed SIN3A was the top decreased gene in lncT-KD embryos. There was a severe blastocyst formation defect in SIN3A-KD embryos. Both lncT and SIN3A could affect NANOG and induce more cell apoptosis. In conclusion, the knockdown of lncT inhibits embryonic development by regulating H3K4me3, H3K4me2, DNA methylation, pluripotency gene, and apoptosis, and SIN3A is one of the downstream genes of lncT in regulating embryonic development.

Deep learning for aspect-based sentiment analysis: a review.

User-generated content on various Internet platforms is growing explosively, and contains valuable information that helps decision-making. However, extracting this information accurately is still a challenge since there are massive amounts of data. Thereinto, sentiment analysis solves this problem by identifying people's sentiments towards the opinion target. This article aims to provide an overview of deep learning for aspect-based sentiment analysis. Firstly, we give a brief introduction to the aspect-based sentiment analysis (ABSA) task. Then, we present the overall framework of the ABSA task from two different perspectives: significant subtasks and the task modeling process. Finally, challenges are proposed and summarized in the field of sentiment analysis, especially in the domain of aspect-based sentiment analysis. In addition, ABSA task also takes the relations between various objects into consideration, which is rarely discussed in the previous work.

ELF4 is critical to zygotic gene activation and epigenetic reprogramming during early embryonic development in pigs.

Zygotic gene activation (ZGA) and epigenetic reprogramming are critical in early embryonic development in mammals, and transcription factors are involved in regulating these events. However, the effects of ELF4 on porcine embryonic development remain unclear. In this study, the expression of ELF4 was detected in early porcine embryos and different tissues. By knocking down ELF4, the changes of H3K9me3 modification, DNA methylation and ZGA-related genes were analyzed. Our results showed that ELF4 was expressed at all stages of early porcine embryos fertilized in vitro (IVF), with the highest expression level at the 8-cell stage. The embryonic developmental competency and blastocyst quality decreased after ELF4 knockdown (20.70% control vs. 17.49% si-scramble vs. 2.40% si-ELF4; p < 0.001). Knockdown of ELF4 induced DNA damage at the 4-cell stage. Interfering with ELF4 resulted in abnormal increases in H3K9me3 and DNA methylation levels at the 4-cell stage and inhibited the expression of genes related to ZGA. These results suggest that ELF4 affects ZGA and embryonic development competency in porcine embryos by maintaining genome integrity and regulating dynamic changes of H3K9me3 and DNA methylation, and correctly activating ZGA-related genes to promote epigenetic reprogramming. These results provide a theoretical basis for further studies on the regulatory mechanisms of ELF4 in porcine embryos.

Player-aware resource compensation in interrupted cricket matches.

The International Cricket Council (ICC) uses the Duckworth-Lewis-Stern (DLS) method for resource compensation in interrupted games, which is an upgraded version of the Duckworth-Lewis (D/L) method. In order to compensate resources, the D/L method uses a generic resource table for all teams without considering both teams' past performance, venue of the match, and players participating in that match. That is why teams cannot strategize according to their strengths and play according to D/L's requirements. This paper presents a method for Player-aware Resource Compensation in Interrupted Cricket Matches (PRCICM). The PRCICM model is dynamic because it allocates a different number of resources to different teams based on their resource usage in the past, players involved in the match and venue of the match. The proposed method eliminates the need to find the generalized scoring patterns with a player-driven approach. A team-specific approach is more appropriate because of teams' different formations and playing approaches.