RACIPE: a computational tool for modeling gene regulatory circuits using randomization.

BACKGROUND: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. RESULTS: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. CONCLUSIONS: We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub ( https://github.com/simonhb1990/RACIPE-1.0 )

Reflexive Regular Equivalence in Bipartite Data

Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose strong structural assumptions. This work presents an unsupervised method for assessing similarity in bipartite data. The method is based on regular equivalence in graphs and uses spectral properties of a bipartite adjacency matrix to estimate similarity in both dimensions. The method is reflexive in that similarity in one dimension informs similarity in the other. The method also uses local graph transitivities, a contribution governed by its only free parameter. Reflexive regular equivalence can be used to validate assumptions of co-similarity, which are required but often untested in co-clustering analyses. The method is robust to noise and asymmetric data, making it particularly suited for cluster analysis and recommendation in data of unknown structure

PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization

In this paper, we present a pure-Python open-source library, called PyPop7, for black-box optimization (BBO). It provides a unified and modular interface for more than 60 versions and variants of different black-box optimization algorithms, particularly population-based optimizers, which can be classified into 12 popular families: Evolution Strategies (ES), Natural Evolution Strategies (NES), Estimation of Distribution Algorithms (EDA), Cross-Entropy Method (CEM), Differential Evolution (DE), Particle Swarm Optimizer (PSO), Cooperative Coevolution (CC), Simulated Annealing (SA), Genetic Algorithms (GA), Evolutionary Programming (EP), Pattern Search (PS), and Random Search (RS). It also provides many examples, interesting tutorials, and full-fledged API documentations. Through this new library, we expect to provide a well-designed platform for benchmarking of optimizers and promote their real-world applications, especially for large-scale BBO. Its source code and documentations are available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io/en/latest, respectively.Comment: 5 page

The association between gene polymorphisms in voltage-gated potassium channels Kv2.1 and Kv4.2 and susceptibility to autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a heritable form of neurodevelopmental disorder that arises through synaptic dysfunction. Given the involvement of voltage-gated potassium (Kv) channels in the regulation of synaptic plasticity, we aimed to explore the relationship between the genetic variants in the KCNB1 and KCND2 genes (encoding Kv2.1 and Kv4.2, respectively) and the risk of developing ASD.MethodsA total of 243 patients with ASD and 243 healthy controls were included in the present study. Sixty single nucleotide polymorphisms (SNPs) (35 in KCNB1 and 25 in KCND2) were genotyped using the Sequenom Mass Array.ResultsThere were no significant differences in the distribution of allele frequencies and genotype frequencies in KCNB1 between cases and controls. However, the differences were significant in the allelic distribution of KCND2 rs1990429 (pBonferroni < 0.005) and rs7793864 (pBonferroni < 0.005) between the two groups. KCND2 rs7800545 (pFDR = 0.045) in the dominant model and rs1990429 (pFDR < 0.001) and rs7793864 (pFDR < 0.001) in the over-dominant model were associated with ASD risk. The G/A genotype of rs1990429 in the over-dominant model and the G/A–G/G genotype of rs7800545 in the dominant model were correlated with lower severity in the Autism Diagnostic Interview-Revised (ADI–R) restricted repetitive behavior (RRB) domain.ConclusionOur results provide evidence that KCND2 gene polymorphism is strongly associated with ASD susceptibility and the severity of RRB

Characterization of a major quantitative trait locus on chromosome five for hundred-kernel weight of maize (Zea mays L)

Kernel weight is one of the most important components of grain yield and is controlled by quantitative trait loci (QTLs) derived from natural variations in maize. However, the molecular roles of QTLs in the regulation of kernel weight have not been fully elucidated. In this study, by using homozygous chromosome single segment substitu- tion lines Z22(SSSL-Z22) as base material, two F populations derived from a cross between elite maize inbred line Zheng58 and SSSL-Z22, were employed to map QTLs of kernel weight traits in two years at the same location. Out of four traits, 3 QTLs were detected in one of the two environments whereas 2 detected in both environments. Two major QTLs, qhkw5-3 for hundred-kernel weight and qkw5-3 for kernel width, were consistently detected in similar chromosome segment in different years. qhkw5-3 was mapped to Bin 5.06 flanked by the SSR markers SYM033 and SYM108 with a genetic interval of 8.8 cM, which made kernel size smaller. qkw5-3 was identified between SYM024 and SYM129 with a genetic interval of 13.9 cM. These results will help to promote the fine map- ping and cloning of the target gene and further develop linked markers to be used in marker-assisted breeding

The safety concerns regarding immune checkpoint inhibitors in liver cancer patients rising mainly from CHB

Aim: To analyze the safety of immune checkpoint inhibitors in primary liver cancer patients and to identify the risk factors for immune-related adverse events (irAEs).Methods: The study enrolled 106 patients with primary liver cancer, including 81 with hepatocellular carcinoma and 25 with intrahepatic cholangiocarcinoma. We analyzed the differences between groups in irAE occurrence, including those with and without targeted drugs and those who received interventional therapy.Results: The incidence of irAEs was 39%, with thyroid function, liver function, and skin events being the most common. There was no correlation among irAE incidence and the liver cancer type, stage, or severity; grade of Child–Pugh score; and Barcelona Clinical Liver Cancer classification. However, being overweight was a significant risk factor for irAEs, correlating with high body mass index. The combination of targeted drugs and/or transcatheter arterial chemoembolization therapy did not increase the incidence of irAEs.Conclusion: Being overweight is a potential risk factor for irAEs in primary liver cancer patients. However, there is no correlation between irAE incidence and the liver cancer type, stage, or severity or a combination of targeted drugs or transarterial chemoembolization therapy