Hand-eye calibration is an important task in vision-guided robotic systems and is crucial for determining the transformation matrix between the camera coordinate system and the robot end-effector. Existing methods, for multi-view robotic systems, usually rely on accurate geometric models or manual assistance, generalize poorly, and can be very complicated and inefficient. Therefore, in this study, we propose PlaneHEC, a generalized hand-eye calibration method that does not require complex models and can be accomplished using only depth cameras, which achieves the optimal and fastest calibration results using arbitrary planar surfaces like walls and tables. PlaneHEC introduces hand-eye calibration equations based on planar constraints, which makes it strongly interpretable and generalizable. PlaneHEC also uses a comprehensive solution that starts with a closed-form solution and improves it with iterative optimization, which greatly improves accuracy. We comprehensively evaluated the performance of PlaneHEC in both simulated and real-world environments and compared the results with other point-cloud-based calibration methods, proving its superiority. Our approach achieves universal and fast calibration with an innovative design of computational models, providing a strong contribution to the development of multi-agent systems and embodied intelligence.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

SUMMARY: The 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression of genes in individual cells due to the massive data volume. Here, we present cellCounts, a new tool for efficient and accurate quantification of Chromium data. cellCounts employs the seed-and-vote strategy to align reads to a reference genome, collapses reads to Unique Molecular Identifiers (UMIs) and then assigns UMIs to genes based on the featureCounts program. Using both simulation and real datasets for evaluation, cellCounts was found to compare favourably to cellRanger and STARsolo. cellCounts is implemented in R, making it easily integrated with other R programs for analysing Chromium data. AVAILABILITY AND IMPLEMENTATION: cellCounts was implemented as a function in R package Rsubread that can be downloaded from http://bioconductor.org/packages/release/bioc/html/Rsubread.html. Data and analysis code used in this study can be freely accessed via La Trobe University's Institutional Repository at https://doi.org/10.26181/21588276.

BACKGROUND: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. RESULTS: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. CONCLUSION: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

A tremendous challenge in the development of Cyber-Physical-Social Systems (CPSS) is to integrate the growing volume and wide variety of data generated from multiple sources. To address the challenge, this paper presents an integration framework on cloud consisting of five functionally complementary processes, namely data representation, dimensionality reduction, relation establishment, data rank and data retrieval. The unstructured, semi-structured and structured data in the cyber, physical and social space are first represented as low-order data tensors, and then transformed to a three-order feature tensor for relation establishment. A similarity-based multi-linear data rank approach is proposed to measure the importance of the CPSS big data, and an incremental method is explored to rapidly and accurately update the rank vector. This paper, through a smart home case study, illustrates a practical application of the proposed integration framework, and evaluates the performance of the multi-linear data rank approach as well as the incremental rank update method. The results reveal that the proposed framework is feasible and competitive to integrate the CPSS big data on cloud.

The multimedia social network (MSN), a combination of the multimedia sharing technology and social network, has prominent social features and diffusion characteristics. Owing to its centerlessness and lack of regulation, MSNs have some serious network environment problems, such as spread of negative digital content and serious data redundancy. To solve the above problems, this paper proposes a strong and weak ties feedback-based trust model in MSNs on the basis of the Weak Ties Theory of sociology. This model evaluates the trust level from two different aspects, multimedia content and user behaviors, and computes the reputation value by the Bayesian estimation principle and the damped window mechanism. On the basis of the trust model, we establish a trust-based information dissemination model in MSNs to study the relationship between trust and digital content dissemination. Simulation results indicate that the trust model is reliable in design, valid in network transmission, and effective in resisting malicious feedback and collusive attacks, enables positive digital data to spread rapidly and widely, and limits the dissemination of negative content.

MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

User-generated content is quickly becoming the greatest source of information on the World Wide Web. Shared content items are initially considered unconfirmed in the sense that their credibility has not yet been established. Conventional, centralized confirmation of credibility is infeasible at the Internet scale and so making use of the annotators themselves to evaluate each item is essential. However, users usually differ in opinions to the same item, and the existence of bias, variance and maliciousness makes the problem of aggregating opinions more difficult. Addressing this problem, we propose the use of an Author-Annotator model with an iterative algorithm, called ScoreFinder, for inferring credibility by ranking shared items. In order to reduce the influence from a variety of error sources, we identify reliable users on each topic, and adaptively aggregate scores from them. Moreover, we transform the users' input to remove errors/anomalies, by identifying patterns of misbehaviour learned from a real data set. We show how our algorithm performs on both real data sets and synthetic data sets, and a significant improvement was achieved in the experiment.