Narrow Results

With the anatomical understanding of the neural connection of the nematode Caenorhabditis elegans (C. elegans), its chemotaxis behaviors are investigated in this paper through the association with the biological nerve connections. The chemotaxis behaviors include food attraction, toxin avoidance and mixed-behaviors (finding food and avoiding toxin concurrently). Eight dynamic neural network (DNN) models, two artifical models and six biological models, are used to learn and implement the chemotaxis behaviors of C. elegans. The eight DNN models are classified into two classes with either single sensory neuron or dual sensory neurons. The DNN models are trained to learn certain switching logics according to different chemotaxis behaviors using real time recurrent learning algorithm (RTRL). First we show the good performance of the two artifical models in food attraction, toxin avoidance and the mixed-behaviors. Next, six neural wire diagrams from sensory neurons to motor neurons are extracted from the anatomical nerve connection of C. elegans. Then the extracted biological wire diagrams are trained using RTRL directly, which is the first time in this field of research by associating chemotaxis behaviors with biological neural models. An interesting discovery is the need for a memory neuron when single-sensory models are used, which is consistent with the anatomical understanding on a specific neuron that functions as a memory. In the simulations, the chemotaxis behaviors of C. elegans can be depicted by several switch logical functions which can be learned by RTRL for both artifical and biological models.

Genetic mechanism behind aging rate uncovered.

China launches survey of endangered porpoises in Yangtze River.

Academic institutes and companies join forces to boost precision nutrition research.

Gigantic DNA database to be built in Nanjing.

Home test kit for cervical screening developed by BGI.

Chinese HPV vaccine gets permission for clinical testing.

Ineffective baby vaccines by two companies recalled.

Robot scores high result on national doctor qualification test.

Head transplant ‘never’ to be allowed.

China ranks second in Global Science Innovation.

Veolia to support Danone Nutricia Wuxi plant in China.

Chinese drug delivery devices market to reach USD638.4 million by 2023.

Zinc oxide nanoparticles (ZnO NPs) are widely used in diverse fields. However, the toxicological effects of ZnO NPs remain inadequately explored. This study aimed to investigate the toxicities of various concentrations of ZnO NPs <50 nm (ZnO NP50), <100 nm (ZnO NP100), and ZnO bulk particles (ZnO BP) after different exposure durations using Caenorhabditis elegans (C. elegans) as an in vivo model, focusing on reproductive capacity, feeding behavior, and lifespan. Polymerase chain reaction (PCR) was conducted to assess expressions of stress-related cep-1 and pmk-1 genes. The results revealed that the effects of ZnO particles on C. elegans’ reproductive capacity were inconsistent after exposure for 24, 48, and 72 h. It was seen a general concentration-dependent reduction in pharyngeal pumping rate, with 1000$\mu$g/mL having the most significant impact across all ZnO particle treatments. Lifespan studies showed no significant differences post exposure of ZnO particles for the mean lifespan of C. elegans, while significant differences for ZnO NP50 treatment at 100$\mu$g/mL and 1000$\mu$g/mL compared to the control and the ZnO BP groups were observed. Finally, PCR results suggested significant up-regulation of cep-1 and pmk-1 gene expression, showing a size-dependent linear trend with ZnO NP50 > ZnO NP100 > ZnO BP. In conclusion, this study underscores the complex size-dependent toxicological effects of ZnO NPs on C. elegans, highlighting significant impacts on pharyngeal pumping rate and gene expression profiles associated with stress response and DNA damage repair pathways. These findings contribute to our understanding of NP toxicity and underscore the importance of size, concentration, and exposure duration considerations in assessing their biological effects.

Regulation of transcription is controlled by sets of transcription factors binding specific sites in the regulatory regions of genes. It is therefore believed that regulatory regions driving similar expression profiles share some common structural features. We here introduce a computational approach for finding a small set of rules describing the presence and positioning of motifs in a set of promoter sequences. This rule set is subsequently used for finding promoters that drive similar expression profiles from a genomic set of sequences. We applied our approach on muscle-expressed genes in Caenorhabditis elegans. We obtained a high average performance, and in the best case we found that almost 50% of true positive test genes scored higher than 90% of the true negative test genes. High scoring non-training sequences were enriched for muscle-expressed genes, and predicted motifs fitting the rules showed a significant tendency to be present in experimentally verified regulatory regions. Our model is more general than existing cis-regulatory module models, as rules selected by our model contain a variety of information, including not only proximal but also distal positioning of pairs of motifs, positioning with regard to the translation start site, and simply presences of motifs. We believe our model can help to increase our understanding about transcription factor cooperation and transcription initiation.