Fully Autonomous Reproduction Robotic System

Self-reproduction robotics systems are capable of producing other robotic systems; such that the resulting systems are fully functional and autonomous. In this poster, a novel method for producing modular robotic systems is presented, where the resulting robots consist of Cubelets - modular robots kit - and the producing robot is built using Lego Mindstorms EV3

XVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving

Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.Comment: 11 pages, 6 figures, accepted by IJCAI 2

Prevalence of foodborne viruses and influenza A virus from poultry processing plants to retailed chickens

IntroductionFoodborne viruses are a serious concern in public health. This study investigated the prevalence of eight foodborne viruses norovirus (NoV), adenovirus (AdV), sapovirus (SapoV), astrovirus, hepatitis A virus (HAV), hepatitis E virus (HEV), rotavirus, aichivirus, and influenza A virus (IAV).Material and methodA total of 316 chicken samples were collected from three poultry processing plants to commercial markets (local and online). RT-qPCR- and PCR-positive amplicons obtained from monitoring were confirmed by sequence analysis.ResultsFoodborne viruses and IAV were not found in poultry processing plants. Of the 100 chickens purchased from the local and online markets, 19 (19.0%) AdV and 2 (2.0%) SapoV were detected. NoV, astrovirus, HAV, HEV, rotavirus, aichivirus, and IAV were not detected in the retailed chickens. Phylogenetic analysis identified 18 human AdV-41, one porcine AdV, and two SapoV-GI.1. It was the first case of the discovery of the SapoV gene in chicken. The average contamination level of detected AdV was 2.4 log DNA copies/g, but there were cases where the highest level was 5.35 log DNA copies/g.DiscussionThis study highlights the importance of chicken's contribution to the transmission of AdV with the possibility of annual variability with emerging symptoms. The prevention of AdV contamination in the food chain from slaughterhouses to retail markets should be monitored and controlled in further study

Apple Ripening Is Controlled by a NAC Transcription Factor

Softening is a hallmark of ripening in fleshy fruits, and has both desirable and undesirable implications for texture and postharvest stability. Accordingly, the timing and extent of pre-harvest ripening and associated textural changes following harvest are key targets for improving fruit quality through breeding. Previously, we identified a large effect locus associated with harvest date and firmness in apple (Malus domestica) using genome-wide association studies (GWAS). Here, we present additional evidence that polymorphisms in or around a transcription factor gene, NAC18.1, may cause variation in these traits. First, we confirmed our previous findings with new phenotype and genotype data from ∼800 apple accessions. In this population, we compared a genetic marker within NAC18.1 to markers targeting three other firmness-related genes currently used by breeders (ACS1, ACO1, and PG1), and found that the NAC18.1 marker was the strongest predictor of both firmness at harvest and firmness after 3 months of cold storage. By sequencing NAC18.1 across 18 accessions, we revealed two predominant haplotypes containing the single nucleotide polymorphism (SNP) previously identified using GWAS, as well as dozens of additional SNPs and indels in both the coding and promoter sequences. NAC18.1 encodes a protein that is orthogolous to the NON-RIPENING (NOR) transcription factor, a regulator of ripening in tomato (Solanum lycopersicum). We introduced both NAC18.1 transgene haplotypes into the tomato nor mutant and showed that both haplotypes complement the nor ripening deficiency. Taken together, these results indicate that polymorphisms in NAC18.1 may underlie substantial variation in apple firmness through modulation of a conserved ripening program

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Using Trend Extrapolation Model to Predict the Needs of Elderly Care Talents in Beijing Institutions

2020-2050 is a period of rapid development of China's population aging, and it is also a critical period for the country to actively respond to population aging. Under the background of the combination of medical care and nursing, institutional elderly care services, as an important branch of the multi-level elderly care service system, have become the main battlefield of the integrated medical and elderly care policy. Therefore, institutional care talents for the aged have also become a key link in improving the quality of life of the elderly population. This paper using trend extrapolation model to predict the needs of elderly care talents in institutions in Beijing, including nursing staff who provide basic living care and professional medical staff who provide services such as rehabilitation, medical treatment, nutrition, and psychological consultation. The results show that, in 2050, the demand for institutional elderly nursing staff in Beijing will exceed 150,000, and the demand for institutional elderly medical staff will reach about 20,000